Spatial Interpolation Methods Research Articles

Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics

Remote sensing, artificial neural networks, and spatial interpolation methods for modelling soil chemical characteristics

Spatial interpolation techniques comparison and evaluation: The case of ground-based gravity and elevation datasets of the central Main Ethiopian rift

The ground-based gravity data reveals diverse anomaly signatures in areas of the Main Ethiopian rift where active volcanic and tectonic activities are dominant. In such a region ground-based data collection is restricted to existing roads and relies on accessible stations. These resulted in gaps in data, either missing, uneven, or insufficient spatial coverage that must be estimated with proper interpolation techniques. Comparison and evaluations of the spatial interpolation methods that are commonly used in potential field geophysical data analysis were made for the terrestrial gravity and elevation data of the central Main Ethiopian rift. In this research, two widely used interpolation techniques, minimum curvature interpolation, and Ordinary Kriging were compared and assessed. A 10 % hold-out validation was employed, where 90 % of the data points were used to generate interpolated surfaces, which were then evaluated against the remaining 10 %. Following interpolation with each technique, the generated grid was converted into discrete data points (estimated values). These are then compared with the available gravity data, which were deliberately excluded from the gridding process (10 % remaining dataset). The accuracy of each method was assessed by evaluation metrics such as mean value, variance, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (r), and R-squared. The results showed that the ordinary Kriging interpolation method outperformed the minimum curvature interpolants for gravity data with all performance metrics, while both interpolants seem to perform equally well for the elevation dataset. Therefore, it is proposed to use the Kriging interpolation method for potential field gravity studies conducted in the central Main Ethiopia rift.

Understanding the structure of crust and shallow upper mantle beneath western Tibet through the joint inversion of Rayleigh wave group velocity dispersion with interpolated receiver functions

We present the 3-D shear wave velocity variationin the crust and upper mantle structure of western Tibet using a joint inversion of Rayleigh wave group velocity dispersion and interpolated receiver function. The lateral sampling of the receiver function and the surface wave dispersion is equalized by the interpolation scheme. This new method of spatial interpolation eliminates difficulties caused by back azimuthal variation in the receiver function. We have jointly inverted these interpolated receiver functions with the Rayleigh wave group velocity dispersions obtained from the earthquake and ambient noise tomography to map the tectonic structure of western Tibet. The study reveals remarkable variation of the seismic characteristics in the crust and shallow upper mantle of western Tibet. The mapped Moho depth is ~74 km in the northern part of the plateau, whereas it is ~69 km in the southern part. A thick mid-crustal slow-velocity zone from ~10km up to ~40 km depth has been observed, which could be attributed to partial melt. A noteworthy finding is the variation in slower crustal velocity between the south and north of the Lhasa Block (LB). We interpret this as the Indian crust that has been underthrusted beneath the Tibetan plateau, and the northern limit of the Indian lithosphere extends to the southern part of the LB.

Evaluating the performance of various interpolation techniques on digital elevation models in highly dense forest vegetation environment

A Digital Elevation Model (DEM) is a numerical representation of the Earth's topography using an array of cells or pixels. DEMs are important for various purposes such as hydrological studies and environmental monitoring in situations where there is a need to assess land use changes, deforestation, and disaster response. In this study, various spatial interpolation methods, such as the Inverse distance weighting (IDW) technique, Natural Neighbor, and Triangulated Irregular Network (TIN) methods, were compared to evaluate the performance in generating accurate and reliable DEMs. We also considered factors such as computational efficiency and ease of implementation to determine the most suitable method. The precision of the generated DEMs was assessed through metrics such as root mean square error (RMSE) and mean absolute bias (MAB). The predicted elevation models were compared with elevation values obtained through ground surveys. Furthermore, a raster agreement analysis was used to compare the elevation values recorded by the two data sources, the USGS 3D Elevation Program (USGS 3DEP) and Unmanned Aerial Vehicle (UAV). The analysis revealed that TIN method demonstrated the highest level of agreement at 92%, while the binning technique showed inconsistency in predicting the elevation values based on the data source. In regions with high forest cover, the results were less accurate due to the limited number of last returns available for developing the DEM. This study also found that TIN recorded the fastest processing time per square acre. The presence of vegetation significantly influences the quantity of Light Detection and Ranging (LiDAR) pulses capable of reaching the forest floor, subsequently impacting the quantity of points available for the creation of a DEM.

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Near real-time interpolative algorithm for modelling air quality in underground mines

As real-time air quality monitoring becomes more prevalent in US underground mines, it is important to provide the highest data reliability with the fewest possible sensors. Real-time sensors remain costly, and these costs are not exclusively financial; the time required to install, calibrate, and maintain real-time sensors poses a large barrier to widespread implementation. Current atmospheric monitoring systems typically rely on displaying point-specific values. This requires operators to infer real-time airborne contamination distributions. Monitoring and control software utilizing mine ventilation network (MVN) solvers has been implemented in limited cases because of their ability to simulate ventilation systems quickly, but these solvers use a one-dimensional representation of the mine, limiting spatial resolution of estimated distributions. Computational fluid dynamics (CFD) has likewise been considered as a means to improve spatial resolution, but processing times prevent its use as a basis for monitoring and control. For the real-time monitoring of airborne contamination distributions, we propose a spatial interpolation method that can estimate the distribution of airborne contaminants in near-real time. This method provides a middle ground between fast processing times and increased spatial resolution. With the use of a pathfinding algorithm and optimization through absolute percentage error minimization, this method outperforms spatial interpolation with a Euclidean distance. By providing contamination distribution information to operators, this method and its derivatives stand to outperform current atmospheric monitoring systems.

The last remnants of the Italian Apennine glaciers: 3-D modeling of the Lower Calderone Glacieret by the IDW spatial interpolation of GPR data

Abstract We present a quantitative ground-penetrating radar (GPR) analysis of the Lower Calderone Glacieret to highlight the recent evolution of one of the southernmost glacial systems in Europe. The Upper and Lower Calderone Glacierets are the last two perennial ice bodies in the Apennine Mountains (Italy), and their continuous monitoring is important for glaciology, hydrology and climate science. We applied a proprietary auto-picking algorithm to track reflections accurately and objectively within three pseudo-3-D GPR data sets that were acquired over the Lower Glacieret in different years. After the time-to-depth conversion, the basal reflections were projected onto the normal versors of a 3-D topographic model of the survey area, at the different GPR trace positions. We then applied an Adjusted Inverse-Distance Weighted Spatial Interpolation method to extrapolate the ice-bedrock interface within the areas not directly covered by the GPR profiles and compare it with the topographic surface to recover the glacieret volume. In this paper, we critically examine the accuracy of the reconstructed models, including possible issues related to the challenging survey areas, such as local artifacts in the interpolated interface caused by irregular GPR coverage. We further discuss the various advantages of the implemented procedure with respect to more traditional glacier monitoring techniques.

Comparative Performance Evaluation of ANN and Hybrid ANN-HBA Models for Temporal and Spatial Modeling of Precipitation in East Azarbaijan Province of Iran

In this study, temporal and spatial modeling of precipitation was performed in East Azarbaijan province of Iran using 27-year (1996-2022) data by employing two neural network models, i.e., stand-alone artificial neural network (ANN) and hybrid ANNHoney badger optimization algorithm (ANN-HBA). In temporal modeling, one-month and two-month precipitation delay steps were used as input parameters, and monthly precipitation was considered as output parameter. On the other hand, in spatial modeling, longitude, latitude, and altitude were used as input parameters and average monthly precipitation was considered as output parameter. Comparative performance of both the employed models was evaluated using three statistical criteria, i.e., root mean square error (RMSE), correlation coefficient (R) and Nash- Sutcliffe efficiency (NSE). In addition, precipitation in ungauged areas was estimated based on rainfall data of the nearby rain gauges through spatial interpolation methods such as isohyet and Thiessen polygon. Results of the temporal precipitation modeling revealed better performance of the hybrid ANN-HBA model over the stand-alone ANN model. Furthermore, the hybrid ANN-HBA model, during testing phase of the temporal modeling, could resemble the precipitation most accurately at Maraghe station (with values of R as 0.95, RMSE as 1 mm, and NSE as 0.85). Similarly, the hybrid ANN-HBA model during testing phase (with values of R as 0.95, RMSE as 1 mm, and NSE as 0.96) outperformed the stand-alone ANN model in depicting the precipitation adequately at spatial scale.

Accuracy and sensitivity of soil erosion estimation using 137Cs technology: A statistical perspective

Random spatial variation of the 137Cs inventory is the principal contributor to uncertainty in soil erosion estimation using 137Cs technology. A statistically sound sampling design is imperative for obtaining reliable soil erosion estimations. The objectives of this study are to: 1) characterize the effect of sample size on the estimates of mean inventories; 2) evaluate the sensitivity of the estimated soil redistribution to sample size using a new 137Cs model; 3) compare erosion detection limits among five conversion models; and 4) assess three spatial interpolation methods. Two small watersheds were sampled in an irregular grid design, and 30 samples were taken on a reference site. A moving window scheme was used to compute the 7-point and 11-point means of 137Cs inventories. The Welch’s modified t-test was used to test the mean estimates and to compute the detection limits for the replicated samples. Five models were selected to convert 137Cs inventories to soil redistribution rates. Since most single samples without replication yielded insignificant test results, the 137Cs technique cannot be used to estimate soil erosion at a single point due to random spatial variation. With 30 reference samples, 7 replicate samples on the sampling site provided reliable estimations of 137Cs inventories and soil redistribution. The spatial patterns of the estimated 137Cs inventories and soil redistribution became more regular and systematic as sample size increased, which agreed increasingly well with topography and surface hydrology. Replicate samples can be taken from each landform element or slope position where the erosion rate is expected to be uniform. Alternatively, a systematic grid sampling scheme can be used, and the nearest neighbors can be treated as replicates to calculate moving averages. The Type I and II errors with replicated samples showed that all sampling positions should be included in calculating watershed or area mean erosion rates even though the Type I errors indicated insignificant redistribution rates for some samples. Due to the lack of spatial autocorrelation, spline and inverse distance weighting methods performed better than kriging for interpolation. Detection limits and magnitudes of soil redistribution varied substantially with conversion models, depending on their sensitivities to changes in 137Cs inventories.

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Compositional kriging analysis: A spatial interpolation method for distributions

Effective development of subsurface petroleum resources relies on the estimation of spatially distributed parameters at undrilled locations. Established geostatistical algorithms based on kriging exist for the estimation of scalar spatial variables such as porosity and permeability. This may not be suitable for the estimation of distributions of properties, such as grain size, whose whole distribution varies spatially. The current conventional approach is to fit a normal or log-normal distribution to the available data, and then to estimate the parameters of the distribution, the mean and standard deviation, spatially using kriging, taking care to consider any dependence between the mean and standard deviation. The assumption that all the grain size distributions can be approximated by a single distribution type is unsatisfactory, since datasets have very different-looking distributions, with variations in skewness, kurtosis, and modality. This paper presents an alternative approach that can handle significant variability in the distribution shape by separating the distribution into bins, like a histogram, and treating these bins as percentages in a composition. Compositional data needs to be mapped onto a simplex to overcome spurious correlations between those components, in addition, spatial estimation methods for compositional data have already been developed. However, the contribution to this field is the mapping of continuous data from distributions into a composition that enables the compositional kriging method to predict distributions at new locations. Moreover, the results showed the prediction distributions in the presence of varying modality, skewness, and kurtosis. Therefore, the grain size datasets in this paper have been working with the confidentiality restrictions so it explains the technique with a dataset of population ages from the US census 2010 for the state of Texas, which shows similar variability in distribution.

Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring.

We propose a novel methane leakage rate remote sensor that combines a single-photon avalanche diode detector with a near-infrared 1653.7 nm low-power laser. The proposed M sequence and triangle wave signal modulation method simultaneously realizes the detection of methane leakage and target point clouds. Innovatively, the sensor's methane concentration and leakage rate quantification ability were simulated by combining the Gaussian plume diffusion model and the Risley prism. The effects of the prism rotation ratio, wind speed, leakage rate, atmospheric stability (AS), target reflectivity, signal averaging period, and concentration spatial interpolation method on leakage rate are discussed. When plume methane concentrations reduce from 10,000 to 500 ppm·m, the relative concentration bias rise from 1% to 30%, the absolute concentration bias is approximately 100 ppm·m. Two spatial concentration interpolation methods introduced leakage rate bias ranging from 6%-25%. For a low AS, the leakage rate bias under the cubic interpolation method was small (approximately 1.6%). In addition, when the initial leakage rate increased from 100 to 1,000 mg/s, the leakage rate bias was approximately 20% smaller.

Research Progress on Spatiotemporal Interpolation Methods for Meteorological Elements

With the development of mathematical statistics, people have developed the spatiotemporal interpolation methods based on the spatial interpolation method or the temporal interpolation method. These methods fully consider the comprehensive effects of time series changes and spatial distribution to better handle complicated and changeable meteorological element data. This article systematically reviews the current research progress of spatiotemporal interpolation methods for spatiotemporal sampling data of meteorological origin. Spatiotemporal interpolation methods of meteorological elements are classified into three categories: spatiotemporal geostatistical interpolation methods, spatiotemporal deterministic interpolation methods, and spatiotemporal mixed interpolation methods. This article summarizes the theoretical concept and practical application of the spatiotemporal interpolation methods of meteorological elements, analyzes the advantages and disadvantages of using spatiotemporal interpolation methods for estimating or forecasting meteorological elements, combined through some measures and their application to explain the accuracy of the spatiotemporal interpolation methods; and discusses the problems and challenges of spatiotemporal interpolation. Finally, the future research focus of spatiotemporal interpolation methods is proposed. This article provides a valuable method reference for estimating or predicting meteorological elements such as precipitation in unsampled points.

Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery

ABSTRACT Snow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths.

A Three-Dimensional Spatial Interpolation Method and Its Application to the Analysis of Oxygen Deficit in the Bohai Sea in Summer

Dissolved oxygen (DO) plays a pivotal role in sustaining marine ecosystems. The Bohai Sea in China is a semi-enclosed sea, and oxygen-deficit events occur from time to time due to human activities. At present, there is a notable absence of any convenient and precise method for obtaining three-dimensional spatial data on DO, and the exploration of the physical mechanisms influencing oxygen deficit remains incomplete. This investigation uses the linear radial basis function (RBF-Linear) fitting method to conduct three-dimensional spatial interpolation for DO, which demonstrates minimal inaccuracy. Then, the RBF-Linear fitting method is utilized to collect a comprehensive three-dimensional spatial dataset encompassing temperature, salinity, and DO in the Bohai Sea in August from 2016 to 2018. The results indicate discernible interannual variations in the extent, area, and distribution of oxygen deficiency during summer in the Bohai Sea. Mechanism analysis reveals that intense precipitation episodes and an increase in wind stress curl exacerbate oxygen depletion. Additionally, the degree, location, and area of the two oxygen-deficit cores (off the Yellow River Estuary and off the Qinhuangdao) in the Bohai Sea are influenced by several factors, including current velocity, direction, local circulation position, and net horizontal transport rate. Furthermore, the study suggests that oxygen deficiency in the Bohai Sea region is currently in its early stages, with a limited degree of injury and a restricted range of influence. The use of a three-dimensional spatial interpolation method to create a complete DO field in three-dimensional space simplifies the research challenges associated with marine oxygen deficit. Moreover, this study holds particular significance for guiding the development of marine fisheries.

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow.

Internal structures of the Last Glacial Period multi-phase cold-climate aeolian dunes, Moravian Sahara, Czechia

Aeolian dunes in the Moravian Sahara developed during at least two distinct cold periods of the Last Glacial Period (LGP). Their two-phase development was previously deduced solely from the different orientations of dune crests without knowing the dunes' internal structure. Therefore, the aim of this study is to reveal the internal structure of the multi-generational dune system, developing a relationship between these dunes and bedrock. A combination of data was obtained from boreholes and ground-penetrating radar (GPR). GPR profiles were measured in places where the boreholes were drilled to obtain high-resolution combination data. Furthermore, models of aeolian sediment thickness and ground-water level were made using a spatial interpolation method (ordinary kriging with spherical semivariogram model) to interpret the GPR results and to investigate a relationship between the bedrock and dune patterns. Internal dune structures confirm the hypothesis of multi-generational development of this dune field. In addition, the aeolian sediment distribution was influenced by the bedrock topography. Finally, this study confirms that a change in LGP atmospheric circulation over Central Europe is the main controlling factor in forming the Moravian Sahara dune field.

Production of fuel-based carbon footprint distribution map using spatial interpolation methods based on GIS

The Earth is a complex system where living and non-living elements coexist in a delicate balance. Climate change is the primary factor responsible for the degradation of this system over time. The far-reaching consequences of climate change impact various aspects of our lives, including the physical environment, urban settings, human activities, economy, technology, agriculture, food production, access to clean water, and public health, all of which are widely acknowledged. Human-induced greenhouse gas emissions in these areas significantly trigger global climate change. Hence, addressing and mitigating the environmental damage from these emissions and the interconnected climate change phenomena is imperative. This situation is where the concept of "carbon footprint" gains prominence in assessing the extent of this damage. Carbon footprint serves as an essential measure in managing and curbing climate change. This study focused on controlling and mitigating carbon emissions, one of the primary greenhouse gasses responsible for climate change, by implementing spatial interpolation techniques based on Geographic Information Systems (GIS). The investigation targeted the Beşirli neighborhood in the Ortahisar district of Trabzon province. Data concerning electricity and natural gas usage were acquired from relevant institutions to perform carbon footprint calculations. Subsequently, carbon footprint calculations were conducted utilizing the acquired data within the specified region. The resulting outputs were systematically organized, integrated into the GIS environment, and linked to their respective geographical locations. Eventually, region-specific carbon footprint distribution maps were generated using selected spatial interpolation methods. These maps enabled a spatial observation of points exhibiting variability in terms of carbon emissions, thereby highlighting the carbon footprints evident in the region. The ultimate goal of this endeavor is to propose practical measures for minimizing the adverse environmental impacts by suggesting strategies to reduce and prevent carbon footprints associated with carbon emissions in the relevant areas.

Distribusi spasial nitrat air tanah di Kecamatan Nanggulan, Kulon Progo, Yogyakarta

Residential areas in Nanggulan sub-district are mainly adjacent to irrigated rice fields. Groundwater in settlements close to irrigated rice fields has the potential to be contaminated with nitrates. Studies on the spatial distribution of groundwater nitrate around irrigated rice fields have never been conducted in Nanggulan District, Kulon Progo Regency. The study was conducted to determine the spatial distribution of nitrates in groundwater in Nanggulan District, Kulon Progo Regency. Nitrate data were collected through groundwater sample testing. The location of groundwater samples is determined using a systematic random sampling. The data was analyzed by spatial interpolation method through Arc GIS 10.6 software. The results showed variations in the spatial distribution of nitrates in the study area. Most of the study area had nitrate levels of 5-10 mg / L distributed in the south. Nitrates exceeding ten mg/L were distributed in the northern part of the study area. Based on research that has been conducted, it is known that 41% of groundwater in the study area is contaminated with nitrates, while 59% of groundwater is not contaminated with nitrates. Spatially, nitrate-contaminated groundwater is found in parts of Tanjungharo Village, some Wijimulyo Village, some Donomulyo Village, some Jatisarono Village, and some Kembang Villages. Groundwater not contaminated with nitrates is found in Banyuroto Village and most areas of Donomulyo Village. Factors that cause nitrate-contaminated groundwater are the location of excavated wells close to intensively fertilized irrigated rice fields, groundwater contained in geological formations of young Merapi volcano deposits, and groundwater depth of less than 5 meters.