Land Surface Elevation Research Articles

Constraining the origin of the Norwegian strandflat – The influence of isostatic and dynamic surface changes

The Norwegian strandflat is a prominent low-relief bedrock surface found near sea level along most of the west coast of Norway. Its origin has been discussed throughout the last 130 years but is yet to be resolved. Some studies suggest that the strandflat represent a tropical weathering front of Mesozoic age that has since been buried and re-exhumed, while others relate its origin to Pleistocene periglacial and glacial processes and/or wave-induced weathering and erosion. Previous interpretations of the strandflat have considered postglacial isostatic uplift, but the impacts of isostatic changes due to glacial erosion and deposition, as well as dynamic surface changes driven by mantle convection, have been largely overlooked. Here we examine how geomorphological-driven isostatic changes and dynamic surface changes have influenced the land-surface elevation along the Norwegian coast during late Pliocene-Quaternary (the last ca. 3 million years). We employ quantitative estimates of glacial erosion and deposition to assess the flexural isostatic response from the resulting load changes. Our analyses show that patterns of geomorphic isostatic adjustments and dynamic surface changes are generally not reflected in the present elevation of the strandflat. Only the loading effect from the deposition of the North Sea Fan can clearly be correlated with the submerged strandflat found near Stad (∼62 °N). Our results imply that if the strandflat formed synchronously along the Norwegian coast as a flat surface at sea level, the strandflat we observe today must have developed after the majority of late Pliocene-Quaternary glacial erosion took place, but prior to the main deposition of the North Sea Fan. This would place strandflat formation within the last few glacial cycles, but before the Last Glacial Maximum (LGM). This inferred pre-LGM age of the strandflat is generally consistent with cosmogenic nuclide exposure ages and observed striations on the strandflat. Finally, we examine ice cover and land-surface changes relative to sea level during the last 80,000 years and find no extended periods favorable for synchronous strandflat formation across all regions along the Norwegian coast. This implies that either the strandflat is diachronous, or that the processes of formation have either been extremely fast under certain conditions or are independent of sea level, for instance related to glacial erosion.

Identification of the Driving factors impacts of Land Surface Albedo over Iran: An analysis with the MODIS data

Albedo is a key parameter in climatic research and depends on environmental and climatic factors. Modeling these factors greatly contributes to understanding environmental variations. To this end, the data of Land Surface Albedo, Land Surface Temperature (LST), Vegetation, Snow, Elevation, Slope, and Aspect of the MODIS sensor from 1/1/2001 to 30/12/2021 with a 1000-m spatial resolution were used. After pre-processing, monthly, seasonal, and annual albedo modeling was performed using multiple linear regression (MLR) in the highlands of Iran. The results of monthly modeling revealed the salient direct role of snow on the albedo of Iran's highlands in all months, except for July, August, and September. In these months, due to the lack of snow coverage and the fruiting of agricultural lands and gardens, the inverse role of vegetation on albedo variations is determining. Seasonal examinations also showed that snow plays a significant role on the albedo of Iran's highlands in winter, spring, and fall; however, vegetation has a determining role in the summer. The annual results indicated that snow, vegetation, elevation, slope, LST, and aspect, respectively, are the factors affecting albedo in the highlands of Iran. Furthermore, the role of snow, LST, and aspect is positive, while the role of vegetation, elevation, and slope is negative on albedo.

Evaluation and improvement of parameterization methods for estimating cloudy-sky downwelling surface longwave radiation from geostationary satellite data

ABSTRACT Estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions presents a significant challenge, the parameterization methods used to estimate cloudy-sky DSLR may yield disparate results for the same scenario. Hence, it is imperative to undertake a comparative and validation study of these methods to comprehend their applicability. This study compares and validates five parameterized schemes for estimating cloudy-sky DSLR using data from the Fengyun-4A (FY-4A) and Himawari-8 geostationary satellites. Additionally, an improved algorithm for cloudy-sky DSLR estimation is proposed, integrating the radiation effect of the entire cloud layer and a nonlinear parameterization algorithm. The verification results demonstrate that the nonlinear parameterization algorithm exhibits higher accuracy compared to those reliant on cloud-base temperature. Among these, the improved algorithm has high accuracy similar to the nonlinear algorithms in BSRN (Baseline Surface Radiation Network) and TPDC (National Tibetan Plateau Data Center) sites, its RMSE are 29.62 and 30.09 W/m2, respectively. Sensitivity analysis reveals that cloud type and cloud-base temperature exert a pronounced influence on DSLR estimation, particularly within parameterization algorithms based on cloud-base temperature, warranting thorough consideration. Furthermore, the influence of land cover type and surface elevation, especially in high-altitude regions with bare surfaces, should not be disregarded in DSLR estimation.

Analyzing the spatial relationship between land surface temperature and elevation in three gullies locations in southeastern Nigeria

Gully erosion is a dangerous phenomenon that threatened human existence, plants and animals communities. Families are being separated from their ancestral home and livelihood due to gully erosion. Also, available agricultural lands to combat food insecurity are contently depleted on yearly basis across the globe as a result of gullies menace. Southeastern Nigeria is highly vulnerable to gully erosions due to it geology and studies on spatial relationships between gully LST and elevation has not been carried out, particular by applying remote sensing and GIS technique to investigate it variations. Available studies where limited to mapping and causes of gully erosion in the region. The study applies LST and elevation data to investigate correlation on three gullies locations. LST was derived from Landsat data and was first converted to radiance image while the elevation data was obtained from Google Earth image. Elevation data was used to produce raster surface in that cell values equivalent to LST at every point can be extracted. Three sets of paired sample points of LST and elevation was extracted from the three gullies locations and used to compute correlation coefficient (r). The study observed at Nanka gully a strong negative correlation of -0.665. This implies an increase in LST correspond to decrease in elevation within the gully. At Ekwolobia and Aguluezechukwu gullies sites, positive correlation coefficient of 0.692 and 0.910 were obtained. The positive correlation coefficients implied that LST and elevation increase or decrease within the gullies. This might be due to unequal distribution of radiation within gully surface as a result of three canopies. For further study, it is recommended that improve elevation data source like LiDAR elevation data should be use to investigate spatial correlation between LST and elevation at gully location.

Integration of Geophysical and Geospatial Techniques to Evaluate Geothermal Energy at Siwa Oasis, Western Desert, Egypt

Environmental degradation is reducing crop productivity in many regions of Egypt. Moreover, unsustainable surface water drainage contributes to salinized soil conditions, which negatively impact crops. Egypt is seeking solutions to mitigate the problem of surface water drawdown and its consequences by exploring renewable and sustainable sources of energy. Geothermal energy and the desalination of saline water represent the only solutions to overcoming the fresh water shortage in agricultural industry and to providing sustainable fresh water and electricity to villages and the Bedouin livelihood. In Egypt, the Siwa Oasis contains a cluster of thermal springs, making the area an ideal location for geothermal exploration. Some of these thermal springs are characterized by high surface temperatures reaching 20 °C to 40 °C, and the bottom-hole temperatures (BHT) range from 21 °C to 121.7 °C. Pre-Cambrian basement rocks are usually more than 440 m deep, ranging from 440 m to 4724.4 m deep. It is this feature that makes the Siwa Oasis locality sufficient for geothermal power production and industrial processes. This study utilized both the Horner and the Gulf of Mexico correction methods to determine the formation temperatures from BHT data acquired from 27 deep oil wells. The present study revealed a geothermal gradient ranging from 18 to 42 °C/km, a heat flux of 24.7–111.3 mW/m2, and a thermal conductivity of 1.3–2.65 W/m/k. The derived geothermal, geophysical, and geological layers were combined together with space data and the topographic layer to map relevant physiographic variables including land surface elevation, depth to basement, lineament density, land surface temperature, and geologic rock units. The ten produced variables were integrated in GIS to model the geothermal potential map (GTP) for the Siwa Oasis region. According to the model, both the eastern side and north and northeastern portions of the study region contain high and very high geothermal potential energy. Combining bottom-hole temperature measurements with satellite remote sensing and geospatial analysis can considerably enhance geothermal prospecting in Egypt and other East African areas that have geologically and tectonically similar settings. In addition to identifying sustainable resources needed for food production, this research has implications for renewable energy resources as well.

Quantifying the influence of climate, host and change in land-use patterns on occurrence of Crimean Congo Hemorrhagic Fever (CCHF) and development of spatial risk map for India

Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.

Climate Change and Urban Poor: Water-related Behavior in Jakarta

Jakarta is a growing urban centre that is facing two problems: climate change and urban poverty. City authority of Jakarta has yet to prepare comprehensive public policies to address the two challenges of the city that has over 9 million population. As a coastal city, Jakarta has a coastline up to 25 km long. The average elevation of land surface is 7 meters above sea level. Around 40 percent of land surface are below the sea level. There are thirteen rivers flowing rainwater from high land of West Java through the city running into Jakarta Bay. Therefore the threat of climate change is not only due to rising sea levels but also flooding and drought. It is estimated that around 1 million of the urban poor experiencing the vulnerability since most of them are illegally occupying the low land surrounding riversides. The extreme weather is now happen frequently both in urban and rural area. Since poor farmers and farm workers have no saving, some of them have trying to get the job in cities such as Jakarta. Their daily income is around USD 6 to USD 11. This article discusses how the urban poor act toward the impact of climate change. Since the urban poor have only subsistence income, they choose to live at the riversides in the all part of the city. They utilize the water resources for many purposes, utilize the short underground water table and also dump the waste into the river. This study discusses what the poor urban know and do regarding the water resources and its relationship with any evidence of climate change impact and vice versa. As an attempt to explore the possibility of linkages with climate change as an anthropogenic behaviour, this study tries to develop how the current water governance effectively address the problem

Techniques to embed channels in finite element shallow water equation models

Two new and novel techniques to efficiently represent channels in large-scale finite element shallow water equation models are introduced. The first enables the discontinuous representation of bathymetric depth by allowing nodes along a discontinuity to have two land surface elevations, one representing the bottom of the discontinuity and the other the top of the discontinuity. Elemental integration proceeds using the nodal depth corresponding to whether the element is on the upper or lower side of the discontinuity and enables efficient treatment of steep sided features in a two-dimensional horizontal discretization. The second technique consolidates nodal equations at paired nodes across a channel. Doing this eliminates cross-channel variability and consequently eliminates the across-channel Courant-Friedrichs-Lewy stability constraint on the model time step. Together the two techniques allow channels of various sizes as well as other instances of steep topography to be embedded seamlessly, efficiently and in a fully coupled manner within an otherwise two-dimensional horizontal spatial discretization. This new capability is especially useful in the interface zone subject to both coastal and hydrological influences as it effectively captures bi-directional channelized flow, bi-directional flow between the channel and the floodplain, and coupled flow when the channel and floodplain are fully submerged. This paper describes the methodological development and presents a series of tests that verify the performance of this novel solution approach.

Reducing vertical bias and error in tidal marsh digital elevation models with machine learning and LiDAR derivatives

The vertical bias and uncertainty of LiDAR-derived elevation data in tidal marshes is a major challenge for researchers in these critical ecosystems. Small positive bias errors in land surface elevation can lead to large underestimates of coastal inundation. Previous studies have shown that the bias and uncertainty of elevation data can be reduced using a variety of statistical techniques. However, many studies cover a relatively small extent, cover a large extent at coarse resolution, or rely on local data products that may not be widely available or are of unknown quality. This study aimed to develop and compare bias reduction approaches for digital elevation models (DEMs) using LiDAR derivatives in tidal marshes along Delaware Bay, Delaware, USA, a region that experiences high rates of relative sea-level rise and frequent coastal flooding. In this study, several statistical and machine-learning approaches were evaluated based on their ability to reduce vertical DEM error using field GPS survey data and LiDAR and terrain derivatives as inputs. The evaluated approaches for reducing DEM error included ensembles of multiple linear regressions, kernel-K Nearest Neighbors, gradient boosted regression trees, random forests, and deep neural networks (DNNs), which were compared based on statistical performance metrics. An ensemble of deep neural networks performed best at removing vertical DEM bias and reducing DEM uncertainty, though other approaches also performed well. GPS survey data indicated a mean absolute error, mean bias, and root mean square error in GPS surveys of 11.9 cm, 10.9, and 14.8 cm, respectively, in the original DEM. These error metrics were reduced to 3.5 cm, −0.2 cm, and 5.1 cm in the DNN ensemble-corrected DEM. The DNN ensemble was then applied to all tidal marshes throughout the study area. This corrected DEM clearly showed how interpretations of marsh platform inundation based on uncorrected DEM surfaces could be misguided. The approach used in this study only requires LiDAR-derived products and field surveys, so it may be applied readily in other regions.

Will They Stay or Will They Go — Understanding South Atlantic Coastal Wetland Transformation in Response to Sea-Level Rise

Threats to coastal wetlands, including sea-level rise and subsidence, led the National Wildlife Refuge (NWR) System to protect over 500,000 hectares of coastal wetlands during the twentieth century, with approximately 20% occurring in the South Atlantic geography. This effort has involved systematic long-term monitoring of changes in marsh elevation using surface elevation tables and marker horizons at 20 sites across 19 NWRs in the southeastern coastal USA. From 2012 to 2021, the rates of change in surface elevation (−9.3 to 7.1 mm/year), accretion (−0.3 to 17.5 mm/year), and net vertical elevation change (−14.3 to 3.1 mm/year) were highly variable among monitoring sites and varied with coastal wetland type (oligohaline marsh, salt marsh, pocosin, or forested wetland), land surface elevation, and estuarine salinity and geomorphology (i.e., tidally influenced or embayed). Of 20 sites included in our study, only six were gaining elevation at a rate that was equal to or greater than the long-term rates of sea-level rise and therefore considered resilient. Only Waccamaw and Currituck NWRs, both located in oligohaline marshes, were gaining elevation at a rate that exceeded sea-level rise by 1 mm/year. These results support the mounting evidence that many coastal wetlands, particularly in the South Atlantic geography of the USA, will undergo ecological transformations in the next several decades. The NWR System and other coastal management entities will need to use strategic decision-making frameworks to identify management actions that can mitigate the loss of coastal wetlands to support the conservation of coastal wetland–dependent and obligate species.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda.

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

Computational biogeographic distribution of the fall armyworm (Spodoptera frugiperda J.E. Smith) moth in eastern Africa

The fall armyworm (FAW), Spodoptera frugiperda J.E. Smith, has caused massive maize losses since its attack on the African continent in 2016, particularly in east Africa. In this study, we predicted the spatial distribution (established habitat) of FAW in five east African countries viz., Kenya, Tanzania, Rwanda, Uganda, and Ethiopia. We used FAW occurrence observations for three years i.e., 2018, 2019, and 2020, the maximum entropy (MaxEnt) model, and bioclimatic, land surface temperature (LST), solar radiation, wind speed, elevation, and landscape structure data (i.e., land use and land cover and maize harvested area) as explanatory variables. The explanatory variables were used as inputs into a variable selection experiment to select the least correlated ones that were then used to predict FAW establishment, i.e., suitability areas (very low suitability – very high suitability). The shared socio-economic pathways, SSP2-4.5 and SSP5-8.5 for the years 2030 and 2050 were used to predict the effect of future climate scenarios on FAW establishment. The results demonstrated that FAW establishment areas in eastern Africa were based on the model strength and true performance (area under the curve: AUC = 0.87), but not randomly. Moreover, ∼27% of eastern Africa is currently at risk of FAW establishment. Predicted FAW risk areas are expected to increase to ∼29% (using each of the SSP2-4.5 and SSP5-8.5 scenarios) in the year 2030, and to ∼38% (using SSP2-4.5) and ∼35% (using SSP5-8.5) in the year 2050 climate scenarios. The LULC, particularly croplands and maize harvested area, together with temperature and precipitation bioclimatic variables provided the highest permutation importance in determining the occurrence and establishment of the pest in eastern Africa. Specifically, the study revealed that FAW was sensitive to isothermality (Bio3) rather than being sensitive to a single temperature value in the year. FAW preference ranges of temperature, precipitation, elevation, and maize harvested area were observed, implying the establishment of a once exotic pest in critical maize production regions in eastern Africa. It is recommended that future studies should thus embed the present study's modeling results into a dynamic platform that provides near-real-time predictions of FAW spatial occurrence and risk at the farm scale.

Active temperate glacial landsystem evolution in association with outwash head/depositional overdeepenings

AbstractThe response of temperate glaciers to rapid climate warming is reflected in the geomorphology (landsystem) resulting from snout recession. This paper develops a locally diverse process‐form model of active temperate glaciers through mapping and quantification of historical landscape change on the Fjallsjökull‐Hrútárjökull foreland, SE Iceland. Quantification of volumetric and areal changes on the foreland are based on time series of archival aerial images for the period 1945–1998, high‐resolution satellite imagery for 2014 and digital elevation models of differences derived from time series of UAV imagery for the years 2014, 2016, 2018, 2019 and 2022. Landscape change and glacier snout behaviour since 1945 highlights the importance of azonal and potentially intrazonal signatures in temperate glacial landsystems, particularly: (1) the development and collapse of partially supraglacial outwash fans to produce outwash heads fronting depositional overdeepenings; and (2) the emergence of ice‐cored eskers that record the evolution of englacial drainage networks operating over overdeepenings. Such landform assemblages are manifested as substantial ice‐cored/hummocky terrains, a characteristic of deglaciating forelands that is likely to be widely replicated wherever ice‐contact glacifluvial assemblages create outwash heads that act as depositional overdeepenings. Due to its significantly greater supraglacial debris cover, complete de‐icing of the Hrútárjökull snout in response to post‐1945 warming was delayed until around 2014. This constitutes a prime example of incremental stagnation, which in a rapidly warming climate has resulted in significant landscape change (land surface elevation collapse of 0.8 m a−1) over the last 8 years.

River hydraulic modeling with ICESat-2 land and water surface elevation

Abstract. Advances in geodetic altimetry instruments are providing more accurate measurements, thus enabling satellite missions to produce useful data for narrow rivers and streams. Altimetry missions produce spatially dense land and water surface elevation (WSE) measurements in remote areas where in situ data are scarce that can be combined with hydraulic and/or hydrodynamic models to simulate WSE and estimate discharge. In this study, we combine ICESat-2 (Ice, Cloud and land Elevation Satellite) land and water surface elevation measurements with a low-parameterized hydraulic calibration to simulate WSE and discharge without the need for surveyed cross-sectional geometry and a rainfall–runoff model. ICESat-2 provides an opportunity to map river cross-sectional geometry very accurately, with an along-track resolution of 0.7 m, using the ATL03 product. These measurements are combined with the inland water product ATL13 to calibrate a steady-state hydraulic model to retrieve unobserved hydraulic parameters such as river depth or the roughness coefficient. The low-parameterized model, together with the assumption of steady-state hydraulics, enables the application of a global search algorithm for a spatially uniform parameter calibration at a manageable computational cost. The model performance is similar to that reported for highly parameterized models, with a root mean square error (RMSE) of around 0.41 m. With the calibrated model, we can calculate the WSE time series at any chainage point at any time for an available satellite pass within the river reach and estimate discharge from WSE. The discharge estimates are validated with in situ measurements at two available gauging stations. In addition, we use the calibrated parameters in a full hydrodynamic model simulation, resulting in a RMSE of 0.59 m for the entire observation period.

High-resolution spatiotemporal modeling of daily near-surface air temperature in Germany over the period 2000–2020

The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (Tair), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of Tair data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (Tmin), mean (Tmean), maximum (Tmax) Tair and diurnal Tair range during 2000–2020. We used weather station Tair observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate Tair from cells with both Tair and LST available. In the second stage, we used this model to predict Tair for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated Tair values to obtain Tair countrywide. All models achieved high accuracy (0.91 ≤ R2 ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R2 ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the Tmean model (0.71 ≤ R2 ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual Tmean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader Tair variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide Tair at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes.

Identifying the Determinants of Distribution of Oncomelania hupensis Based on Geographically and Temporally Weighted Regression Model along the Yangtze River in China.

Background: As the unique intermediate host of Schistosoma japonicum, the geographical distribution of Oncomelania hupensis (O. hupensis) is an important index in the schistosomiasis surveillance system. This study comprehensively analyzed the pattern of snail distribution along the Yangtze River in Jiangsu Province and identified the dynamic determinants of the distribution of O. hupensis. Methods: Snail data from 2017 to 2021 in three cities (Nanjing, Zhenjiang, and Yangzhou) along the Yangtze River were obtained from the annual cross-sectional survey produced by the Jiangsu Institute of Parasitic Diseases. Spatial autocorrelation and hot-spot analysis were implemented to detect the spatio–temporal dynamics of O. hupensis distribution. Furthermore, 12 factors were used as independent variables to construct an ordinary least squares (OLS) model, a geographically weighted regression (GWR) model, and a geographically and temporally weighted regression (GTWR) model to identify the determinants of the distribution of O. hupensis. The adjusted coefficients of determination (adjusted R2, AICc, RSS) were used to evaluate the performance of the models. Results: In general, the distribution of O. hupensis had significant spatial aggregation in the past five years, and the density of O. hupensis increased eastwards in the Jiangsu section of the lower reaches of the Yangtze River. Relatively speaking, the distribution of O. hupensis wase spatially clustered from 2017 to 2021, that is, it was found that the border between Yangzhou and Zhenjiang was the high density agglomeration area of O. hupensis snails. According to the GTWR model, the density of O. hupensis was related to the normalized difference vegetation index, wetness, dryness, land surface temperature, elevation, slope, and distance to nearest river, which had a good explanatory power for the snail data in Yangzhou City (adjusted R2 = 0.7039, AICc = 29.10, RSS = 6.81). Conclusions: The distribution of O. hupensis and the environmental factors in the Jiangsu section of the lower reaches of the Yangtze River had significant spatial aggregation. In different areas, the determinants affecting the distribution of O. hupensis were different, which could provide a scientific basis for precise prevention and control of O. hupensis. A GTWR model was prepared and used to identify the dynamic determinants for the distribution of O. hupensis and contribute to the national programs of control of schistosomiasis and other snail-borne diseases.

Spatial Variations of Village-Level Environmental Variables from Satellite Big Data and Implications for Public Health–Related Sustainable Development Goals

The United Nations Sustainable Development Goals (SDGs) include 17 interlinked goals designed to be a blueprint for the world’s nations to achieve a better and more sustainable future, and the specific SDG 3 is a public health–related goal to ensure healthy living and promote well-being for all population groups. To facilitate SDG planning, implementation, and progress monitoring, many SDG indicators have been developed. Based on the United Nations General Assembly resolutions, SDG indicators need to be disaggregated by geographic locations and thematic environmental and socioeconomic characteristics for achieving the most accurate planning and progress assessment. High-resolution data such as those captured at the village level can provide comparatively more precise insights into the different socioeconomic and environmental factors relevant to SDGs, therefore enabling more effective sustainable development decision-making. Using India as our study area and the child malnutrition indicators stunting, underweight, and wasting as examples of public health–related SDG indicators, we have demonstrated a process to effectively derive environmental variables at the village level from satellite big datasets on a cloud platform for SDG research and applications. Spatial analysis of environmental variables regarding vegetation, climate, and terrain have shown spatial grouping patterns across the entire study area, with each village group having different statistics. Correlation analysis between these environmental variables and stunting, underweight, and wasting indicators show a meaningful relationship between these indicators and vegetation index, land surface temperature, rainfall, elevation, and slope. Identifying the spatial variation patterns of environmental variables at the village level and their correlations with child malnutrition indicators can be an invaluable tool to facilitate a clearer understanding of the causes of child malnutrition and to improve area-specific SDG 3 implementation planning. This analysis can also provide meaningful support in assessing and monitoring SDG implementation progress at the village level by spatially predicting SDG indicators using available socioeconomic and environmental independent variables. The methodology used in this study has the potential to be applied to other similar regions, especially low-to-middle income countries where a high number of children are severely affected by malnutrition, as well as to other environmentally related SDGs, such as Goal 1 (No Poverty) and Goal 2 (Zero Hunger).

Quantitative determination of environmental factors governing the snow melting: a geodetector case study in the central Tienshan Mountains

Because of the distinctive vertical climate and topography gradients in the alpine region, the snow cover of the Tienshan Mountains possesses complex spatiotemporal heterogeneity, particularly during the melting process. Quantifying the environmental factors is therefore crucial to understanding the melting process and for predicting and managing snowmelt runoff. Herein, the snow cover area, grain size, and contamination extent were determined to characterize the detailed melting status based on surface reflectance data of MOD09A1 in the central Tienshan Mountains from 2013 to 2017. The environmental factors collected include relief (elevation, slope, and aspect); meteorology (surface air temperature, land surface temperature, solar radiation, and wind speed); and land surface vegetation. Analysis of the geodetector results indicated the following. (1) Patterns of changes in the overall dominant environmental variables were consistent for the pre-, mid-, and post-melting periods defined according to the decline of snow cover area over five years. (2) The overall major environmental factors were wind speed and radiation (pre-period), land surface temperature and elevation (mid-period), and elevation and land surface types (post-period), respectively. (3) Regional distinctions were detected of the dominant environmental factors. In the pre-melting period, the effects of solar radiation and wind speed were noticeable in the north and south regions, respectively. The effects of elevation, land surface temperature, and land cover types became more prominent in all regions during the mid- and post-melting periods. (4) Interaction between the major environmental factors was significantly enhanced on both the overall and regional scales, thus affecting the snow-melting process. Finally, the energy distribution mismatch resulted in the snowmelt. Multiple environmental factors substantially affect heat redistribution at different spatiotemporal scales, resulting in the snowmelt as a complex manifestation of the factors and their interactions. The findings highlight regional differences in various environmental factors of the melting process and offer a theoretical foundation for the melting process at various scales over multiple years.

Evaluasi Fungsi Sistem Drainase Kawasanan Permukiman Kota Tolitoli (Studi Kasus Rt 001.002-Rw 005 Kelurahan Baru)

In principle, roads in Tolitoli Regency have been equipped with a drainage system. From the results of the field survey, the data needed as a reference for evaluating the drainage system are obtained, namely the dimensions of the channel, the area of ​​the study area as primary data and land surface elevation, population data as secondary data. The data obtained were analyzed in order to obtain different soil surface heights, different heights at each point of the drainage channel, the dimensions of the current drainage channel along with the depth of sediment in the drainage channel, so that the cause of the malfunction of the drainage system can be determined. The cause of non-functioning drainage in urban residential areas is due to sedimentation at the bottom of the channel which causes drainage not optimal. So it is necessary to maintain the function of the existing drainage channels in urban residential areas so that they can function optimally.

Hydraulic calibration with ICESat-2 land and water surface elevation observations

Hydraulic calibration with ICESat-2 land and water surface elevation observations