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Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained by inverse distance weighting (IDW). Second, the maximal information coefficient (MIC) can adequately measure the degree of correlation between meteorological data and inflow; therefore, the MIC can distinguish key attributes from massive meteorological data and further reduce the computational burden. Last, LSTM is chosen as the prediction method due to its powerful nonlinear predictive capability, which can couple historical inflow records and meteorological data to forecast inflow. The Xiaowan hydropower station is selected as the case study. To evaluate the effectiveness of the M-LSTM for runoff prediction, several methods including LSTM, meteorological data backpropagation neural network (M-BPNN), meteorological data support vector regression (M-SVR) are employed for comparison with the M-LSTM and six evaluation criteria are used to compare its performance. Results revealed that M-LSTM outperforms other test methods in developing the long-term prediction method. HIGHLIGHTS A highly accurate long-term inflow forecasting framework is developed using meteorological data based on long short-term memory neural network.; The meteorological dataset of the study area is obtained using inverse distance weighting.; The maximal information coefficient can adequately measure the degree of correlation between meteorological data and inflow.; The proposed framework demonstrates superior forecast performance.;

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The accuracy of MODIS IR PWV is poor. • The GPS station and MODIS pixel points do not coincide completely in point position. • The problems of spatial interpolation and calibration model are considered in MODIS calibration process. • Compared with the original MODIS IR PWV, the accuracy of the new model is greatly improved in all aspects. • MODIS IR PWV of the new model is in good agreement with GPS PWV. The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions where surface water resources are scarce, groundwater is crucial. Accurate mapping of groundwater depth is vital for sustainable management practices. This study evaluated the performance of three spatial interpolation techniques – inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF) – in predicting groundwater depth distribution across Dire Dawa City, Ethiopia. The results demonstrated the superiority of the RBF method, exhibiting the lowest RMSE (3.21 m), MAE (0.16 m), and the highest R2 (0.99) compared to IDW and OK. The IDW method emerged as the next best performer (RMSE = 4.68 m, MAE = 0.16 m, R2= 0.97), followed by OK (RMSE = 5.32 m, MAE = 0.42 m, R2= 0.95). The RBF's superior accuracy aligns with findings from other semi-arid regions, underscoring its suitability for data-scarce areas like Dire Dawa. This comparative evaluation provides valuable insights for selecting the optimal interpolation method for groundwater depth mapping, supporting informed decision-making in local water resource management.The methodological approach comprised: • Implementation of three interpolation techniques, namely, inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF), utilizing 56 groundwater depth measurements from locations dispersed throughout the study area. • Cross-validation through randomly withholding 20 % of the data for validation purposes. • Comparison of the techniques based on statistical measures of accuracy, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2).

Australia is a unique continent, surrounded by the ocean, and the majority of its catchments flow to the coast. Some of these catchments are gauged and others are ungauged. There are 405 gauged catchments covering 2,549,000 km2 across the coastal regions of 12 drainage divisions in Australia, whereas there are 771 catchments conceptualised as ungauged covering additional 835,000 km2. The spatial and temporal distribution of mean annual rainfall and potential evaporation (PET) vary significantly from one drainage division to another. We developed a continuous daily streamflow time series of all gauged and ungauged catchments from 1993 onwards. We applied the daily GR4J lumped conceptual model to these catchments. The performance of gauged catchments was analysed through (i) visual inspection of daily hydrographs, flow duration curves, and daily scatter plots; and (ii) performance metrics, including NSE and PBias. Based on the NSE and PBias, performance ratings of 80% and 96% of the models, respectively, were found to be 'good'. There was no relationship found between the catchment area and the model performance. The ungauged catchments were divided into four categories based on distance from potential donor catchments, where observed data are available for GR4J model calibration, and Köppen climate zone. The total ungauged catchments represent 24.7% of the total drainage division areas. The streamflow from ungauged catchments was estimated using the GR4J model based on the parameters of their donor catchments. Overall, runoff ratios from ungauged catchments were found to be higher compared to their donor-gauged catchments, likely driven by their higher rainfall and less PET. This tendency was particularly evident in two drainage divisions—the Carpentaria Coast (CC) and the Tanami–Timor Sea Coast (TTS)—where ungauged areas comprised 51% and 43%, respectively. The mean gauged annual streamflow varied significantly across drainage divisions—230 gigalitres (GL) from the South Australian Gulf (SAG) to 146,150 GL in TTS. The streamflow from all ungauged catchments was estimated at 232,200 GL per year. Overall, the average streamflow from all drainage divisions, including gauged and ungauged areas, across the coastal regions of Australia was estimated at 419,950 GL per year. This nationwide estimate of streamflow dataset could potentially enhance our understanding of coastal processes and lead to improvements in marine modelling systems and tools. [ABSTRACT FROM AUTHOR]

With the development of computer science, more and more complex problems rely on the help of computers for solving. When facing the parameter optimization problem of complex models, traditional intelligent optimization algorithms often require multiple iterations on the target problem. It can bring unacceptable costs and resource costs in dealing with these complex problems. In order to solve the parameter optimization of complex problems, in this paper we propose a multi-level surrogate-assisted optimization algorithm (MLSAO). By constructing surrogate models at different levels, the algorithm effectively explores the parameter space, avoiding local optima and enhancing optimization efficiency. The method combines two optimization algorithms, differential evolution (DE) and Downhill simplex method. DE is focused on global level surrogate model optimization. Downhill simplex is concentrated on local level surrogate model update. Random forest and inverse distance weighting (IDW) are constructed for global and local level surrogate model, respectively. These methods leverage their respective advantages at different stages of the algorithm. The MLSAO algorithm is evaluated against other state-of-the-art approaches using benchmark functions of varying dimensions. Comprehensive results from the comparisons showcase the superior performance of the MLSAO algorithm in addressing expensive optimization problems. Moreover, we implement the MLSAO algorithm for tuning precipitation parameters in the Community Earth System Model (CESM). The outcomes reveal its effective enhancement of CESM's simulation accuracy for precipitation in the North Indian Ocean and the North Pacific region. These experiments demonstrate that MLSAO can better address parameter optimization problems under complex conditions. [ABSTRACT FROM AUTHOR]

The significant reduction of surface water resources and recent recurrent droughts have increased the reliance on groundwater sources, leading to a decline in their quality. In this study, 28 samples of well water from different locations in Khanmirza, Lordegan, Borujen, Ardal and Kiar were collected in 2020-2021 and underwent chemical analysis in the laboratory. To evaluate the quality of the samples for use in agricultural, drinking and industrial sectors, the Wilcox diagram, Schuler diagram and Langlier index were used. Using interpolation methods, the chemical properties of the samples taken within the study area were examined and qualitative zoning maps were prepared using Kriging and inverse distance weighting methods. The results showed that according to the Wilcox diagram, most of the samples are within the range of C2S1 and are suitable for agricultural purposes. According to the Schuler diagram, these samples are within the range of good quality for drinking and based on the Langlier index, they are within the range of precipitating and consumable. This study helps decision-makers to have a clear view and plan comprehensively and effectively for the exploitation and preservation of groundwater resources, taking into account the qualitative maps of groundwater.

Different forms of inorganic phosphorus in surface soils can be released into water or adsorbed into sediment in response to various environmental factors. Although the Sanhekou Reservoir is still under construction, the periodic wetting-drying events may result in the release of inorganic phosphorus from the soil into the water once it is completed. Therefore, this study aimed to investigate the background soil values and estimate the phosphorus release fluxes of the Puhe River, Wenshuihe River, and Jiaoxihe River of the reservoir. We would like to provide a theoretical basis for the subsequent management and ecological protection of the reservoir based on this study. The Chang–Jackson chemical continuous extraction method was used to determine the forms of inorganic phosphorus in the soil, and fluorescence data were obtained by excitation–emission matrix combined with parallel factor (EEM-PARAFAC). The results showed that there were four types of phosphorus in the soil of the reservoir, including calcium-bound phosphate, aluminium-bound phosphate, iron-bound phosphate, and occluded phosphate. The total quantities of these phosphorus types were measured to be 6.71 × 104, 1.14 × 104, 0.91 × 104, and 0.49 × 104t, respectively. Additionally, it was observed that there was 2.12 × 104 t of bioavailable phosphorus present in the soil. HIGHLIGHTS EEM-PARAFAC was used to determine the fluorescence components in soil along constructed reservoirs.; Phosphorus fluxes and phosphorus release risks in soil along constructed reservoirs were predicted.; The potential of eutrophication of water in the reservoir was predicted.;

Abstract The soil composition in the Yellow River Subaquatic Delta is complex, and soil liquefaction is a serious threat to the safety of offshore projects. Two-dimensional soil liquefaction evaluation cannot meet the practical needs of engineering. In this paper, in-situ cone penetration tests (CPT) were used to investigate the classification and distribution of soils. Liquefaction variability prediction analysis was performed for the region by the inverse distance weighting (IDW) and full-cokriging methods using the liquefaction vulnerability index (LVI) and cone tip resistance as sample parameters. The results show that: (1) the overall soil grain size in the offshore direction is coarser than that in the far shore, with the clay content less than 18% at Station S1 in the near shore, but more than 90% at Stations S10 and S5 in the far shore; (2) using IDW to predict the liquefaction potential, which first decreases and then increases in the offshore direction, the one-dimensional reconsolidation settlement (SV1D) predicts better than the other two LVIs; (3) a full-cokriging method with normalized cone tip resistance as the main variable and sleeve resistance (fs) as the auxiliary variable is proposed, and its prediction accuracy is better than that of the ordinary kriging (OK) method. [ABSTRACT FROM AUTHOR]

This study focuses on the physical and chemical properties of soils and their geographical distribution, with a specific focus on red clay. The inverse distance weighting (IDW) technique, integrated with Geographic Information Systems (GIS), was employed to predict the chemical characteristics of the soil. Sampling was conducted at twenty-one locations in three areas: Bor Mountain, Jambor, and Kirkuk Hills, all located within Kirkuk City. Seven soil properties were examined: acidity, organic matter content, total dissolved salts (TDS), gypsum, chlorides, and sulfates. The chemical analysis revealed that the soil pH ranged within an acidic range. One sample exhibited a high TDS level. Chloride levels varied within a specific range. The concentration of organic matter in the soil exhibited variability. Sulfur trioxide and gypsum concentrations were found to be below average in the study region. The IDW technique effectively mapped the distribution of the different soil parameters within Kirkuk City, demonstrating a range from good to excellent accuracy. Additionally, a cross-validation method was employed to assess the correlation between the fundamental and investigated chemical properties. The results showed good to excellent degrees of correlation in the different structures studied. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrological models require complete and accurate weather data time series to represent watershed‐scale responses adequately. The Global Historical Climatology Network (GHCN) is the most comprehensive weather database used in hydrological modelling studies globally. Since higher‐density, lower‐reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modellers in the United States have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for the estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW) and Closest Station), creating a seamless time series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) and Thornthwaite‐Mather models for 21 US Department of Agriculture‐Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010–2021. Except for three watersheds, all of the SWAT models had Nash‐Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from −25% to 25% with a satisfactory performance rating. IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using novel combined GHCN data with all Closest Station, IDW and IDEW methods. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing has been one of the most important technologies to monitor soil salinization. However, soil moisture can also interfere with the acquisition of field spectral images and then the prediction of soil salinity. The aim of this study was to effectively eliminate the effect of soil moisture, thereby improving the inversion accuracy of soil salinity. Field spectral reflectance (Ref) was collected from the Yinchuan Plain of China. The soil salinity was then measured to analyze the spectral characteristics of soils with different moisture content. The first derivative of reflectance (FDR), orthogonal signal correction (OSC), and their combination (FDR-OSC) were also employed to transform the spectral data. A systematic evaluation was performed on the correlations of original and transformed spectral data with the soil salinity and moisture content. A soil salinity inversion model with the support vector machine (SVM) was finally established after removing the moisture effect. The results showed that: 1) The overall change in soil salinity was striking (0.30-14.98 g/kg), indicating a strong variability across the study area. Soil pH ranged from 7.82 to 9.58, with a small standard deviation and weak variability. Soil moisture content was ranged between 2.49% and 29.29%, representing a moderate variability. 2) An inverse relationship emerged between soil moisture content and the original spectral reflectance, with the three absorption bands near 1 430, 1 950, and 2 200 nm, respectively. The principal absorption wavelength was near 1 950 nm, indicating a long-wave drift pattern. The reflectance values were reduced for the better drift of the absorption valley using the OSC, compared with Ref. The bandwidth and depth of the three moisture absorption bands all converged, despite the lower differential reflectance values from the FDR transformation curve. 3) The correlation between soil spectral data and moisture content was followed the descending order: Ref, OSC, FDR, FDR-OSC, indicating that FDR-OSC performed better than OSC and FDR to remove the moisture effect. The correlation between soil spectral data and salinity was followed the descending order: FDR-OSC, FDR, OSC, Ref, indicating that the highest sensitivity of spectral data was transformed by FDR-OSC to soil salinity. Only five bands in the 400-2 400 nm wavelength range of Ref were selected as the sensitive bands (R 2≥0.50, P<0.05), much fewer than 11 and 18 sensitive bands of OSC and FDR, respectively. The highest number of sensitive bands was obtained by FDR-OSC, reaching 26. The increased sensitive bands of OSC, FDR, and FDR-OSC mainly comprised near-infrared long waves. 4) Compared with the Ref-based model, the soil salinity inversion model with FDR-OSC performed better fitting and inversion, as the modeling coefficient of determination(Rc² ), verification of coefficient of determination (Rp² ), and relative prediction deviation (RPD) were 0.952, 0.960 and 5.04, respectively. 5) Spatial interpolation by inverse distance weighting revealed that the slightly and moderately salinized soils covered 76.0% of the Yinchuan Plain, whereas the strongly salinity soil and salinized soils only accounted for 2.5%. The areas with the high soil salinity were concentrated mainly in the central and northwestern parts of the study area, with the low soil salinity in the northeastern and southern parts. In conclusion, the FDR-OSC can provide an effective way to eliminate the effect of soil moisture on spectral reflectance, thereby improving the inversion accuracy of soil salinity. The findings can also provide scientific evidence for the high-precision monitoring of soil salinization using hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

Abstract Flow duration curve represents the percentage of time that a river flow is equal to or greater. As these curves provide a direct response to the behavior of water resources in a basin, which is used widely in hydropower projects, it is important to predict flow duration curves in no metering basins, named “ungagged basins.” The geostatistical approach to predict the values of these curves in non-measured stations shows the expansion of the range of studies in this topic. The aim of this study is to predict the flow duration curve over long periods of time in a basin with ungauged regions using probability kriging, inverse distance weighting (IDW) and maximum likelihood (ML) methods. Flow data from 38 flow measuring stations in the Dez Basin were used to map different discharges of the flow duration curve, and as a result, in order to complete their values, zone and quantify them, three different values of Q 10, Q 50 and Q 90 of the flow duration curve acquired. The results show that as the flow rate increases (or the time percentage decreases), the amount of computational error increases and in all cases, the probability kriging method has a smaller error (0.96) than the IDW (1.65) and ML (1.15) methods.

Flow duration curve represents the percentage of time that a river flow is equal to or greater. As these curves provide a direct response to the behavior of water resources in a basin, which is used widely in hydropower projects, it is important to predict flow duration curves in no metering basins, named "ungagged basins." The geostatistical approach to predict the values of these curves in non-measured stations shows the expansion of the range of studies in this topic. The aim of this study is to predict the flow duration curve over long periods of time in a basin with ungauged regions using probability kriging, inverse distance weighting (IDW) and maximum likelihood (ML) methods. Flow data from 38 flow measuring stations in the Dez Basin were used to map different discharges of the flow duration curve, and as a result, in order to complete their values, zone and quantify them, three different values of Q10, Q50 and Q90 of the flow duration curve acquired. The results show that as the flow rate increases (or the time percentage decreases), the amount of computational error increases and in all cases, the probability kriging method has a smaller error (0.96) than the IDW (1.65) and ML (1.15) methods. [ABSTRACT FROM AUTHOR]

Thematic maps are essential tools in precision agriculture to demonstrate the information of spatially distributed phenomena. A thematic map can be created from sampling data, a standard procedure for soil attributes. Interpolation methods are used to estimate data in unknown locations, such as inverse distance weighting (IDW) and ordinary Kriging (OK). For both interpolators, it is essential to use the appropriate parameters to estimate values in non-sampled locations, either the exponent value and the number of neighbors for IDW, or the theoretical model adjusted to the experimental semivariogram for OK. Thus, this trial aims at adopting additional criteria in selecting interpolators and evaluating their performance. AgDataBox platform's data interpolation module was improved, where the process of selecting the interpolator and determining its parameters considers the criteria (i) effective spatial dependence index, (ii) the first semivariance significance index, and (iii) slope of the model ends index. The experimental data come from an experiment in two agricultural areas in Brazil, using grids with good sampling density (2.7, 2.6, and 3.5 points per ha). It was observed that, usually, the application method of the three new criteria selected different models for a dataset and this must be considered in the interpolator selection process. Thematic maps varied from 0.1 to 64%, according to the coefficient of relative deviation, when comparing the three methods of applying the selection criteria. [ABSTRACT FROM AUTHOR]

Rainfall data are crucial in hydrology models. In this study, the assessment of two spatial interpolation approaches of Inverse Distance Weighting (IDW) and Local Polynomial Interpolation (LPI) for rainfall in Peninsular Malaysia was conducted. The daily precipitation for 515 rainfall stations across Peninsular Malaysia during 2011–2020 was used as the reference data. The performance of IDW and LPI was evaluated by the computation of the coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE). The results show that LPI methods surpass IDW methods on the annual scale rainfall interpolations in Peninsular Malaysia by exhibiting a better statistical evaluation. [ABSTRACT FROM AUTHOR]

Resource estimation is one of the most important steps in the mining process. Precise resource estimation has a significant influence on the optimization of subsequent mining steps, i.e. mine planning and scheduling. The previous resource estimation in the Angouran Mine was conducted based on the provided information in the local coordinate system which causes considerable errors in estimations. Therefore, an attempt is made in this research to optimize the block model of the Angouran Mine and resource estimation based on the information in the UTM global coordinate system. For this purpose, exploratory data is firstly transferred from the local coordinate system to the UTM environment. Then, block model optimization is conducted using indicator kriging (IK) in which the waste blocks are removed and the block model was optimized. Finally, resource estimation is performed using the inverse distance weighting (IDW) and simple kriging (SK) methods. After variogram analyses in different directions, it was found that the mine deposit is anisotropic. Also, validation results showed that the acquired correlation coefficient in the carbonate and sulfide sections for IDW, SK and IK is 0.86, 0.87 and 0.92, and 0.88, 0.87 and 0.92, respectively. Finally, the obtained grades and tonnages are compared with the actual data of the exploratory boreholes, mined blocks and previous resource estimation in the mine. Comparative results showed that the obtained grades and tonnages from both previous and new models are over-estimated and higher than the actual values. The minimum errors of grade estimation equal 46% and 23.1% for previous and new resource estimations (before and after the waste removal), respectively. Also, the mining errors of tonnage estimation are 50.29% and 28.37% for previous and new models, respectively. This field comparison proved that transferring the exploratory data to the UTM system, utilization of the IK to remove the waste blocks and applying the SK for resource estimation lead to the optimization of the block model and a reduction in the estimation error compared to the previous estimations for the mine.

The combination of model predictive control (MPC) and learning methods has been gaining increasing attention as a tool to control systems that may be difficult to model. Using MPC as a function approximator in reinforcement learning (RL) is one approach to reduce the reliance on accurate models. RL is dependent on exploration to learn, and currently, simple heuristics based on random perturbations are most common. This paper considers variance-based exploration in RL geared towards using MPC as function approximator. We propose to use a non-probabilistic measure of uncertainty of the value function approximator in value-based RL methods. Uncertainty is measured by a variance estimate based on inverse distance weighting (IDW). The IDW framework is computationally cheap to evaluate and therefore well-suited in an online setting, using already sampled state transitions and rewards. The gradient of the variance estimate is then used to perturb the policy parameters in a direction where the variance of the value function estimate is increasing. The proposed method is verified on two simulation examples, considering both linear and nonlinear system dynamics, and compared to standard exploration methods using random perturbations.

Australia is a unique continent, surrounded by the ocean, and the majority of its catchments flow to the coast. Some of these catchments are gauged and others are ungauged. There are 405 gauged catchments covering 2,549,000 km2 across the coastal regions of 12 drainage divisions in Australia, whereas there are 771 catchments conceptualised as ungauged covering additional 835,000 km2. The spatial and temporal distribution of mean annual rainfall and potential evaporation (PET) vary significantly from one drainage division to another. We developed a continuous daily streamflow time series of all gauged and ungauged catchments from 1993 onwards. We applied the daily GR4J lumped conceptual model to these catchments. The performance of gauged catchments was analysed through (i) visual inspection of daily hydrographs, flow duration curves, and daily scatter plots; and (ii) performance metrics, including NSE and PBias. Based on the NSE and PBias, performance ratings of 80% and 96% of the models, respectively, were found to be ‘good’. There was no relationship found between the catchment area and the model performance. The ungauged catchments were divided into four categories based on distance from potential donor catchments, where observed data are available for GR4J model calibration, and Köppen climate zone. The total ungauged catchments represent 24.7% of the total drainage division areas. The streamflow from ungauged catchments was estimated using the GR4J model based on the parameters of their donor catchments. Overall, runoff ratios from ungauged catchments were found to be higher compared to their donor-gauged catchments, likely driven by their higher rainfall and less PET. This tendency was particularly evident in two drainage divisions—the Carpentaria Coast (CC) and the Tanami–Timor Sea Coast (TTS)—where ungauged areas comprised 51% and 43%, respectively. The mean gauged annual streamflow varied significantly across drainage divisions—230 gigalitres (GL) from the South Australian Gulf (SAG) to 146,150 GL in TTS. The streamflow from all ungauged catchments was estimated at 232,200 GL per year. Overall, the average streamflow from all drainage divisions, including gauged and ungauged areas, across the coastal regions of Australia was estimated at 419,950 GL per year. This nationwide estimate of streamflow dataset could potentially enhance our understanding of coastal processes and lead to improvements in marine modelling systems and tools.

As energy demand continues to increase, the environmental pollution problem is becoming more severe. Governments and researchers have made great efforts to avoid and reduce air pollution. The prediction of PM2.5, as an important index affecting air quality, has great significance. However, PM2.5 concentration has a complex change process that makes its prediction challenging. By calculating both PM2.5 concentration and that of other pollutants in the atmosphere and meteorological factors, it is evident that the variation in PM2.5 concentration is influenced by multiple factors, and that relevant features also influence each other. To reduce the calculated loss, with full consideration given to the influencing factors, we used the maximum correlation and minimum redundancy (MRMR) algorithm to calculate the correlation and redundancy between features. In addition, it is known from the Brock–Dechert–Scheinman (BDS) statistical results that the change in PM2.5 is nonlinear. Due to the outstanding performance of bidirectional long short-term memory (BiLSTM) neural networks in nonlinear prediction, we constructed an encoder–decoder model based on BiLSTM, named ED-BiLSTM, to predict the PM2.5 concentration at monitoring stations. For areas without monitoring sites, due to the lack of historical data, the application of neural networks is limited. To obtain the pollutant concentration distribution in the study area, we divided the study area into a 1 km × 1 km grid and combined the ED-BiLSTM model via the use of the inverse distance weighting (IDW) algorithm to obtain the PM2.5 concentration values in a region without monitoring stations. Finally, ArcGIS was used to visualize the results. The data for the case study were obtained from Xi'an. The results show that, compared with the standard long short-term memory (LSTM) model, the RMSE, MAE, and MAPE of our proposed model were reduced by 24.06%, 24.93%, and 22.9%, respectively. The proposed model has a low error for PM2.5 prediction and can provide a theoretical basis for the formulation of environmental protection policies. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precise adjustments of farm management activities, such as irrigation and soil treatment according to site-specific conditions, are crucial. With advances in smart agriculture and sensors, it is possible to reduce the cost of water and soil treatment inputs but still realize optimal yields and high-profit returns. However, achieving precise application requirements cannot be efficiently practiced with spatially disjointed information. This study assessed the potential of using an electromagnetic induction device (EM38-MK) to cover this gap. An EM38-MK was used to measure soil apparent electrical conductivity (ECa) as a covariate to determine soil salinity status and soil water content θ post irrigation at four depth layers (Hz: 0–0.25 m; Hz: 0–0.75 m; Vz: 0.50–1 m). The inverse distance weighting method was used to generate the spatial distribution thematic layers of electrical conductivity. The statistical measures showed an R2 = 0.87; r > 0.7 and p ≤ 0.05 on correlation of ECa and SWC. Based on the South African salinity class of soils, the area was not saline ECa < 200 mS/m. The EM38-MK can be used to estimate soil salinity and SWC variability using ECa as a proxy, allowing precise estimations with depths and in space. These findings provide key information that can aid in irrigation scheduling and soil management. [ABSTRACT FROM AUTHOR]

Stubble burning in Punjab, India, poses significant environmental challenges, particularly impacting air quality. This study aims to examine the spatial and temporal patterns of stubble burning events and their potential effect on ambient air quality from 2019 to 2022. High-resolution Sentinel-2 satellite imagery was employed to delineate the spatial extent of stubble burning. Burnt areas were identified using the Normalised Burn Ratio (NBR). Air quality was evaluated based on PM2.5 and PM10 concentrations data obtained from the Punjab Pollution Control Board. The Inverse Distance Weighting (IDW) interpolation technique was used to estimate pollution values in areas lacking direct monitoring. The study revealed significant year-to-year variations in areas affected by stubble burning. The smallest burnt areas were recorded in October 2019 and 2021 (209 sq km), while the largest was in 2020 (755.38 sq km). In every year studied, the burnt area in November consistently exceeded that in October, with the largest area (10315 sq km) observed in 2021. PM2.5 and PM10 concentrations also showed annual fluctuations, with the highest recorded in 2020 and 2021. In particular, in October 2020, higher PM2.5 and PM10 levels were detected in the eastern region of Punjab. November consistently exhibited higher PM2.5 and PM10 concentrations than October for all years analysed, peaking in 2021. The spatial and temporal variations of stubble burning events and their relationship with air quality highlight the need for targeted interventions. Understanding these patterns is crucial for mitigating the adverse effects of stubble burning on air quality in Punjab, India. Future research should focus on evaluating the effectiveness of various mitigation strategies. [ABSTRACT FROM AUTHOR]

Hydrological modeling is an effective tool for predicting the hydrological response of watersheds in order to develop appropriate water resource management strategies. Various modeling techniques are available to simulate rainfall-runoff processes in ungauged basins, including regionalization of hydrologic model parameters. Regionalization by spatial proximity (SP) and physical similarity (PS) were chosen for this study to be used with Hydrologic Modeling System (HEC-HMS), which is semi-distributed hydrological model, to evaluate the performance of the model in simulating sub-basin flows as well as the applicability of averaging methods in the case of ungauged sub-basins. Eight sub-basins belonging to the large Cheliff watershed were selected using available data from the period 2007 to 2012. In order to perform a controlled regionalization, one of the eight sub-basins (Wadi Tikzal) was assumed to be ungauged, and five sub-basins were selected to be donors by the (SP) regionalization method and five others by the (PS) regionalization. The results were compared to the original gauged sub-basin series. The performance analysis was carried out through the Nash-Sutclife Efficiency (NSE), the coefficient of determination (R²) and the root mean squared error (RMSE). The results of the simulation are generally satisfactory for wadi Tikzal sub-basin. The model adequately simulated the flows in the other sub-basins, during both calibration and validation phases. The results obtained showed that the regionalization methods used in this study, with the arithmetic mean and the inverse distance weighting (IDW), yielded good results with NSE and R² values exceeding 0.75 and RMSE values were close to 0.20. The arithmetic mean gave higher results compared to the IDW method, the mean of NSE between the two methods is 0.68 for the arithmetic mean and 0.65 for IDW, and R² of 0.69 for the arithmetic mean and 0.65 for IDW. The obtained results demonstrate that the regionalization by spatial proximity and physical similarity, using the HECHMS hydrological model can be effectively used to predict streamflow in ungauged watersheds, leading to effective water resources management, which enriches the literature regarding the flows regionalization, averaging methods and HEC-HMS performance, in ungauged sub-basins and especially in the northern Algerian region. [ABSTRACT FROM AUTHOR]

Spatial interpolation methods are frequently used to characterize soil attributes’ spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.

Abstract Key message: A harmonized, comprehensive meteorological time series for 78 German intensive forest monitoring plots (Level II) has been made available from 1961 to 2019. The used hybrid spatial interpolation routine using simple linear regression and inverse distance weighting allows for gap filling of missing data and also for extrapolation outside measurement period to analyze long-term effects of climate on forest ecosystems. The dataset is available at https://www.openagrar.de/receive/openagrar_mods_00079174 . The associated metadata are available at: https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/433a028f-dfc8-4a7c-82af-b8d7efafd724 .

Drought is among the natural disasters that seriously impact the environment and human life. This study aims to explore the spatial pattern of drought using the percent of normal precipitation index (PNPI) in Fars Province, located in the Southern part of Iran. To this end, a drought risk model based on data from 42 stations in Fars province from 1990 to 2019 was evaluated. The model includes three criteria of maximum drought intensity in the period, drought trend, and a maximum number of consecutive dry years. The final drought risk map was obtained with an arithmetic mean of three indicators of intensity, continuity, and trend. The final hazard map and the 3-criteria map were interpolated by the inverse distance weighting (IDW) method and were classified into five risk classes: none, mild, moderate, severe, and very severe. The final vulnerability map shows that moderate hazard areas (5% of the region), which are observed in the Sothern parts of the region, are less widespread than areas under severe hazard (83% of the region), which are observed in almost all parts of the region. According to the final vulnerability map, about 94% of the area of Fars province is under severe and very severe conditions. Overall, this study, regarding its simplicity and considering different dimensions of drought, may be utilised as a basic framework to evaluate drought hazards for other locations worldwide. In this respect, it is necessary to study the multiple sights of this phenomenon for land use planning, resource management, and prevention of water and food crises. Therefore, this model can help users and administrations with executive initiatives. [ABSTRACT FROM AUTHOR]

Aquatic environmental problems, such as algae, turbid water, and poor oxygen content, have become increasingly common. In river analysis, hydrological and water quality characteristics are used for evaluating aquatic ecological health, which necessitates continuous monitoring. In addition, because measurements are conducted using a fixed measurement method, the hydrological and water quality characteristics are not investigated for the entire river. Furthermore, obtaining high-resolution data is tedious, and the measurement area and time are limited. Hence, low-resolution data acquisition is generally preferred; however, this requires an appropriate interpolation method to obtain a wide range of data. Therefore, a 3D interpolation method for river data is proposed herein. The overall hydraulic and water quality information of a river is presented by visualizing the low-resolution measurements using spatial interpolation. The Kriging technique was applied to the river mapping to improve the mapping precision through data visualization and quantitative evaluation. [ABSTRACT FROM AUTHOR]

For the designation of nitrate vulnerable zones under the EU Nitrate Directive, some German federal states use inverse distance weighting (IDW) as interpolation method. Our study quantifies the accuracy of IDW with respect to the designation of areas with a groundwater nitrate concentration above the threshold of 50 mg NO3/l using a dataset of 5790 groundwater monitoring sites in Bavaria. The results show that the absolute differences of nitrate concentrations between the monitoring sites are only weakly correlated within a range of no more than 0.4 km. The IDW cross-validated nitrate concentration of measurement sites shows a mean absolute error of 7.0 mg NO3/l and the number of measurement sites above 50 mg NO3/l is 44% too low by interpolation for all sites as a whole. The corresponding values for interpolation separately for the 18 hydrogeological regions in Bavaria are 7.1 mg NO3/l and 38%. The sensitivity and the accuracy of nitrate concentration maps due to the variation of IDW parameters and the position of sampling points are analysed by Monte Carlo IDW interpolations using a Random Forest modelled map as reference spatial distribution. Compared to this reference map, the area with a concentration above 50 mg NO3/l in groundwater is estimated by IDW to be 46% too low for the best IDW parametrization. Overall, IDW interpolation systematically underrates the occurrence of higher range nitrate concentrations. In view of these underestimations, IDW does not appear to be a suitable regionalization method for the designation of nitrate vulnerable zones, neither when applied for a federal state as a whole nor when interpolated separately for hydrogeological regions. [ABSTRACT FROM AUTHOR]

Spatial interpolation is a required method to generate a continuous surface such as Digital Elevation Model (DEM) because field investigation for most of the surface's part is time-consuming with a high demand in both human resources and monetory cost. One of the most used deterministic interpolation models is Inverse Distance Weighting (IDW) model. The model takes several neighbors' information, and the weights are constructed based on the distance between the interpolated point and the neighbors' points. From the machine learning model, Artificial Neural Networks (ANNs) model has also been used for spatial interpolation. The input of ANNs model is also one of the parameters that need to be defined when building the model. This paper evaluated the effect of the number of inputs (neighbors) on the elevation interpolation accuracy. We applied IDW and ANNs to interpolate the elevation of Balikpapan City, Indonesia. The results show that the accuracy increases significantly when the number of inputs is between one and three. However, after three inputs, additional input would not change the accuracy significantly. ANNs performed better than IDW. For three or more inputs, the MAE of ANNs and IDW interpolations are below 1.1 and around 2 meters, respectively. [ABSTRACT FROM AUTHOR]

The particulate matter with a diameter of 2.5 μm (PM2.5) concentration seriously impacts the environment, climate, and human health. PM2.5 emissions are caused by anthropogenic or natural sources and are still a problem worldwide. In this study, monthly, seasonal, and annual spatial distributions of PM2.5 concentrations were analyzed over Iraq for the period 2003 to 2020 by use of the geographic information system technique. The results indicated that the PM2.5 concentration was higher in summer than in winter and autumn. The monthly mean maximum values of PM2.5 occurred during June and July with 8.4 and 8.7 μg/m³ over central and southern regions of Iraq, respectively, while monthly means of minimum values were observed during November over northern and western regions. These results conclude that the magnitude of outdoor PM2.5 concentrations varies among seasons and regions. Also, the annual means of PM2.5 were less than the standard maximum permissible limits, and their seasonal means were smaller than this limit at all seasons. [ABSTRACT FROM AUTHOR]

Spatial interpolation is a required method to generate a continuous surface such as Digital Elevation Model (DEM) because field investigation for most of the surface’s part is time-consuming with a high demand in both human resources and monetory cost. One of the most used deterministic interpolation models is Inverse Distance Weighting (IDW) model. The model takes several neighbors’ information, and the weights are constructed based on the distance between the interpolated point and the neighbors’ points. From the machine learning model, Artificial Neural Networks (ANNs) model has also been used for spatial interpolation. The input of ANNs model is also one of the parameters that need to be defined when building the model. This paper evaluated the effect of the number of inputs (neighbors) on the elevation interpolation accuracy. We applied IDW and ANNs to interpolate the elevation of Balikpapan City, Indonesia. The results show that the accuracy increases significantly when the number of inputs is between one and three. However, after three inputs, additional input would not change the accuracy significantly. ANNs performed better than IDW. For three or more inputs, the MAE of ANNs and IDW interpolations are below 1.1 and around 2 meters, respectively.

A gridded maximum and minimum (Tx and Tn) daily temperature dataset derived by spatial downscaling and bias correction of the ERA5-Land (ERA5L) for the period 1981–2010 is presented. Observed daily Tx and Tn at 154 stations in Ethiopia covering record lengths of 5–30 years were used as a reference. The statistics that define the Gaussian distribution (mean and standard deviation) of Tx and Tn from the station observations were interpolated in space to create a monthly climatology and interannual statistics at 0.05° × 0.05° resolution using a hybrid interpolation approach that combines linear regression with topographic and location attributes, and non-Euclidean inverse distance weighting interpolation. The interpolated monthly and interannual statistics were then used to debias the ERA5L Tx and Tn using a quantile mapping approach. Leave-one-out cross-validation showed that the mean absolute errors in the corrected and downscaled daily temperatures are about 0.7 °C for Tx and 1.1 °C for Tn, reducing the statistical biases in the ERA5L Tx and Tn by 68% and 25% respectively. For monthly climatology, 40–64% of the biases were removed for Tx while for Tn the reductions range from 19% to 32%. The correction also improved commonly used indices for extremes like the probability of warm days, cold days, and warm nights, but overestimated the probability of cold nights. The presented open-access Tx and Tn dataset is a substantial improvement over existing gridded temperature datasets for Ethiopia, such as ERA5L and the Climate Hazards Infrared Temperature with Station (CHIRTS), and we suggest it is suitable for a wide range of environmental applications, e.g. in the fields of hydrology, agriculture, and ecology.

Key message: A harmonized, comprehensive meteorological time series for 78 German intensive forest monitoring plots (Level II) has been made available from 1961 to 2019. The used hybrid spatial interpolation routine using simple linear regression and inverse distance weighting allows for gap filling of missing data and also for extrapolation outside measurement period to analyze long-term effects of climate on forest ecosystems. The dataset is available at https://www.openagrar.de/receive/openagrar%5fmods%5f00079174. The associated metadata are available at: https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/433a028f-dfc8-4a7c-82af-b8d7efafd724. [ABSTRACT FROM AUTHOR]

The application of a deep learning algorithm (DL) can more accurately predict the initial flowering period of Platycladus orientalis (L.) Franco. In this research, we applied DL to establish a nationwide long-term prediction model of the initial flowering period of P. orientalis and analyzed the contribution rate of meteorological factors via Shapely Additive Explanation (SHAP). Based on the daily meteorological data of major meteorological stations in China from 1963–2015 and the observation of initial flowering data from 23 phenological stations, we established prediction models by using recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used as training effect indicators to evaluate the prediction accuracy. The simulation results show that the three models are applicable to the prediction of the initial flowering of P. orientalis nationwide in China, with the average accuracy of the GRU being the highest, followed by LSTM and the RNN, which is significantly higher than the prediction accuracy of the regression model based on accumulated air temperature. In the interpretability analysis, the factor contribution rates of the three models are similar, the 46 temperature type factors have the highest contribution rate with 58.6% of temperature factors' contribution rate being higher than 0 and average contribution rate being 5.48 × 10−4, and the stability of the contribution rate of the factors related to the daily minimum temperature factor has obvious fluctuations with an average standard deviation of 8.57 × 10−3, which might be related to the plants being sensitive to low temperature stress. The GRU model can accurately predict the change rule of the initial flowering, with an average accuracy greater than 98%, and the simulation effect is the best, indicating that the potential application of the GRU model is the prediction of initial flowering. [ABSTRACT FROM AUTHOR]

Ips typographus, which was first identified in 1984 in the oriental spruce forests of Artvin, Turkey, has killed millions of cubic metres of trees. Severe outbreaks of I. typographus following storms resulting in fallen trees and salvage logging done as a part of control activities have caused gaps in the stand canopy of oriental spruce forests. These forests, which have many important functions, such as supplying water, soil conservation and preventing natural disasters in the area they spread, have a fragmented structure because of beetle damages and various traditional utilization practices of the local community. In this study, the local spread of I. typographus in the forests of Hatila Valley National Park and the neighbouring Taşlıca Forest Sub‐District was evaluated, and the areas susceptible to the beetle were determined using the frequency ratio method. A total of 120 pheromone traps were employed in the study. A beetle density map of the study area was produced using the inverse distance weighting interpolation method based on the total number of beetles caught by the traps. Then, the correlation between the regions with high I. typographus density and environmental factors that contributed to this situation were determined using the frequency ratio method. An average 6528.70 beetles were caught by the pheromone traps as a result of this study. The density of the beetles was high in 60.64% of the study area, and an average 10,844.25 beetles were caught by the pheromone traps in these areas. The difference in the total number of beetles between areas with high beetle density and those with low or medium beetle density was significant. It was determined that 32.53% of the study area had very high or high susceptibility to I. typographus. Different parameters such as altitude, slope, aspect, distance to forest roads, stand type, topographic wetness index and area solar radiation were found to be associated with the susceptibility of an area to I. typographus. [ABSTRACT FROM AUTHOR]

Context: Obtaining accurate and precise maps of landscape features often requires intensive spatial sampling and interpolation. The data required to generate reliable interpolated maps varies with sampling density and landscape heterogeneity. However, there has been no rigorous examination of sampling density relative to landscape characteristics and interpolation methods. Objectives: Our objective was to characterize the 3-way relationship among sampling density, interpolation method, and landscape heterogeneity on interpolation accuracy and precision in simulated and in situ landscapes. Methods: We simulated landscapes of variable heterogeneity and sampled at increasing densities using gridded and random strategies. We applied three local interpolation methods (i.e., Inverse Distance Weighting, Universal Kriging, and Nearest Neighbor) to the sampled data and estimated accuracy (slope and intercept) and precision (R2) between interpolated surfaces and the original surface. Finally, we applied these analyses to in situ data using a normalized difference vegetation index raster collected from pasture with various resolutions. Results: In our simulations, all interpolation methods and sampling strategies yielded similar accuracy and precision except in the case of Universal Kriging with random sampling. Additionally, low heterogeneity and increasing sample density improved both accuracy and precision, with cross-validation slopes and R2 values approaching optimal values. In situ analysis demonstrated that heterogeneity decreased with resolution. Nearest Neighbor under both sampling strategies and Universal Kriging using the gridded sampling strategy had the highest accuracy and precision. Decreased heterogeneity and increased sampling density improved accuracy and precision for all combinations of interpolation method and sampling strategies. Conclusions: Heterogeneity of the landscape is a major influence on the accuracy and precision of interpolated maps. There is a need to create structured tools to aid in determining sampling design most appropriate for interpolation methods across landscapes of various heterogeneity. [ABSTRACT FROM AUTHOR]

Groundwater is the drinking water source for the majority of rural settlements of district Nowshera, Khyber Pakhtunkhwa, Pakistan. The study aimed to analyze the groundwater quality and its vulnerability to pollution and to develop its spatial distribution mapping. For this purpose, forty-eight groundwater samples were collected from dug wells, tube wells, and hand pumps of sixteen villages and analyzed for physicochemical parameters. The XY coordinates of the sample's sources were marked by Magellan Triton 1500 handheld global positioning system (GPS). The results were compared with WHO and Pak-EPA guidelines. The results of the majority of selected parameters were found within the WHO and Pak-EPA guidelines; however, in certain areas the concentration of total dissolved solids (TDS), chlorides (Cl−), and alkalinity were higher than the guideline limits. Based on cumulative water quality the excellent water quality prevails over an area of 376 km2 (21% of district area), good water quality 726 km2 (42%), poor 424 km2 (24%), very poor 116 km2 (6%), and unfit for drinking 84 km2 (4%). The water of the Nizampur and Rashaki areas were categorized unfit for drinking. The groundwater quality of nearly one-half of the district varies from poor to very poor, and the soil type and vadose zone sediment/material was found the key reason for groundwater contamination. Based on the infiltration capacity of vadose zone material, the study area was divided into four water pollution vulnerable zones. The low vulnerable zone covers an area of 104 km2, moderate 862 km2, high 667 km2, and very high 93 km2. The most important factor which determines the vulnerability of the groundwater to contamination is the vadose zone material/sediment which in turn determines the soil infiltration capacity. The generated groundwater susceptibility and water quality maps provide critical information for identifying optimal locations for supply wells. [ABSTRACT FROM AUTHOR]

The Special Region of Yogyakarta is one of the provinces in Indonesia with a large number of communal wastewater treatment plants (CWWTPs). However, less than 40% of CWWTPS in the province are able to be operated well. This study aims to determine the effectiveness of CWWTPs concerning the quality of the surrounding groundwater. The research location was in Mlati, one of three sub-districts with the highest number of CWWTPs and highest population density in the province. Technical data were analyzed in a laboratory, including water quality of CWWTPs and groundwater quality. This study also analyzed non-technical data related to CWWTP management. These data were collected through in-depth interviews with CWWTP managers and users. The data were analyzed systematically, not only quantitatively but qualitatively. The distribution of water quality from CWWTP treatment and groundwater was mapped using inverse distance weighting (IDW). The results show that the CWWTP effluent meets quality standards for pH, temperature, COD, oil and grease, and ammonia. However, some CWWTPs fail to meet the quality standards for the TSS and E. coli parameters. Groundwater has an acidic pH, with ammonia, nitrite, and zinc parameters meeting quality standards, but oil and grease, COD, nitrate, and Pb concentrations exceed quality standards in several wells. This study indicates that the presence of CWWTPs is not correlated with the quality of groundwater due to the inefficiency of the CWWTPs, the unmet coverage area, and the conditions of sanitation facilities at the location. [ABSTRACT FROM AUTHOR]

In the present study, simple and ordinary kriging methods, inverse distance and linear regression based on digital elevation model of the earth were evaluated for estimating annual rainfall using twenty-year statistics of precipitation data (1998-2018) in 33 rainfall stations in Chaharmahal and Bakhtiari province. For this purpose, first in ArcMAP, for each model in Kriging method, its variogram was calculated and using two-way evaluation technique, the error of the maps was estimated. The best method among geostatistical methods was conventional kriging method with Gaussian model. MAE, MBE and RMSE statistical indices for this method were 74.44, 0.48 and 93.72, respectively. Then, rainfall and altitude data of the stations were used using a linear regression model in Curve Expert environment. Finally, in order to determine the best model for spatial distribution of precipitation as well as comparing statistical and geostatistical methods, linear regression and ordinary kriging models were compared with each other and the MAE, MBE and RMSE statistical indices for regression method obtained were 115, 3 and 155, respectively. As a result, due to the accuracy, precision and error rate of the prepared maps, the most suitable method for interpolation of annual precipitation is the conventional kriging method with Gaussian model.

The study described in this paper involves the application of a conventional resource estimation method, inverse distance weighting (IDW), and univariate geostatistical technique, ordinary kriging (OK) to the gold grades data from the modified palaeoplacer Teberebie gold deposit, in Ghana. The deposit consists of 4 layered well-defined orebodies referred to as A reef, CDE reef, F24 reef and G reef at the mine environment. Simple, reliable, and adequately accurate resource/reserve estimation are essential to mining operations. Data used for the research were collected by diamond and reverse circulation (RC) drilling. A total of 19353 one-meter composite samples, consisting of 18962 RC chip samples from 695 RC drill holes, and 391 diamond drill core samples from 11 DD holes. Samples were analysed by atomic absorption spectrometry (AAS) for gold (Au). Descriptive statistical treatment was conducted on grade values for the reefs. To analyse for spatial structure of Au mineralisation, experimental downhole, and several horizontal directional semi-variograms were computed, and models fitted. Ore reserves were estimated by OK and IDW methods, and results of the various reefs compared. Regression analysis of estimated results indicate that, the inverse distance square (ID2) model produced estimates that compared well with the OK model in all the ore zones. It is therefore, appropriate to use ID2 as an alternative estimation method to the OK method for purposes of mine planning and grade control.

• An error decomposition and fusion framework is proposed for daily precipitation estimation. • Geographically weighted regression and geographical difference analysis is implemented. • Improved precipitation estimates are achieved by the merging product. • The merging product demonstrates its superiority in accurately estimating winter precipitation and high-value precipitation. • The influence of GPCC station data on the accuracy of the merging product has been considered. Near-real-time satellite precipitation products (SPPs) possess inherent application prospects in the hydro-meteorological field due to their convenient acquisition and accessibility. Integrating gauge-based measurements with near-real-time SPPs is an effective approach for achieving precise spatial precipitation estimates at daily scale. This study proposed a newly developed error decomposition and fusion framework, named EDGWR, which integrates error decomposition and geographically weighted regression (GWR). The final merged product, denoted as IMERG-EDGWR, was obtained from the near-real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-E) product. IMERG-EDGWR was compared with the raw IMERG-E, near-real-time IMERG-L, post-real-time IMERG-F, global multi-source merged precipitation data (MSWEP), interpolated results (IDW and OK), and direct application of GWR outcomes (IMERG-GWR), utilizing the daily ground measurements from 12 meteorological stations located in the Yellow River source region (YRSR). The evaluation results from 2014 to 2018 revealed that IMERG-EDGWR exhibited significant enhancements over the raw IMERG-E, surpassed the research-level IMERG-F, and generally outperformed IDW, OK, MSWEP, and IMERG-GWR. Notably, IMERG-EDGWR enhances the detection of heavy precipitation events, refining estimates of both magnitude and frequency for precipitation over 25 mm. During the winter season, IMERG-EDGWR produced the most accurate precipitation estimates with notable improvement of precipitation detection capabilities. An experiment reducing input data by excluding gauge observations from the GPCC dataset tested the robustness of the EDGWR algorithm, confirming its superiority even with diminished input data. The merging framework proposed in this study constitutes an efficacious and implementable solution to enhance the accuracy of near-real-time SPPs and is expected to be implemented in different regions in future research. [ABSTRACT FROM AUTHOR]