Accurate prediction of soil temperature (Ts) is critical for efficient soil, water and field crop management. In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Existing machine learning models have high performance, but suffer from uncertainty and instability in prediction. Therefore, GLUE approach was implemented to quantify model uncertainty, while wavelet coherence was used to assess interactions between Ts and meteorological parameters. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve Ts prediction accuracy and reduce model uncertainty. For both arid and semi-humid sites, ANFIS-SFO produced the most accurate performance at studied soil depths. At best, hybridization with SFO (ANFIS-SFO, MLP-SFO, RBFNN-SFO, SVM-SFO) decreased RMSE by 5.6%, 18%, 18.3%, and 18.2% at 5 cm, 11.8%, 10.4%, 10.6%, and 12.5% at 10 cm, and 9.1%, 12.1%, 13.9%, and 14.2% at 30 cm soil depth compared with the respective standalone models. GLUE analysis confirmed the superiority of hybrid models over the standalone models, while the hybrid models decreased the uncertainty in Ts predictions. ANFIS-SFO covered 95%, 94%, and 96% observation data at 5, 10, and 30 cm soli depths, respectively. Wavelet coherence analysis demonstrated that air temperature, relative humidity, and solar radiation, but not wind speed, had high coherence with Ts at different soil depths at both sites, and meteorological parameters mostly influenced Ts in upper soil layers. In conclusion, uncertainty analysis is a necessary and powerful technique to obtain an accurate and realistic prediction of Ts. In contrast, wavelet coherence analysis is a useful tool to investigate the most effective variables that strongly affect predictions.

GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables

This paper aims to develop, in a case of multiple water sources, an optimal operational strategy that can be used in arid/semiarid regions by water decision-makers to schedule the use of each source over time, to control the artificial recharge to aquifers, to improve the energy efficiency related to the water supply system, to determine the optimal amount of water each source should supply over a period, and to minimise the water costs. In this regard, a novel, cost effective, and advanced optimal controller to operate a water supply system in arid/semiarid regions has been developed. This model is designed as a multi-constraint non-linear programming model that meets the demand for an ever-growing population, and considers in its formulation multiple surface water sources, aquifers, desalination plants, recycled water plants, the seasonal availability of surface water and groundwater, the monthly rainfall variability, and the seasonal energy price. To verify the effectiveness of the developed model, a real case study was conducted. The results obtained showed a 3% reduction in the water supply cost and sustainable improvement in groundwater management, demonstrating the model’s capacity to manage aquifer recharge efficiently, to optimally schedule the use of water sources. Adopting this strategy improves water security and the energy security in an urban region as it decreases the use of energy-intensive water sources during seasons with high electricity demand.

Optimisation Approach Toward Water Management and Energy Security in Arid/Semiarid Regions

Partial Wavelet Coherence to Evaluate Scale-dependent Relationships Between Precipitation/Surface Water and Groundwater Levels in a Groundwater System

Drought Assessment Using Gridded Data Sources in Data-Poor Areas with Different Aridity Conditions

Salinitas permukaan laut (Sea Surface Salinity, SSS) merupakan salah satu faktor oseanografi yang penting untuk diteliti. SSS erat kaitannya dengan daerah penangkapan ikan dan fenomena upwelling. SSS bervariasi secara temporal dan spasial. Penelitian ini bertujuan untuk mengetahui variasi temporal, spasial, dan periode dominan dari SSS di perairan selatan Kebumen. Data SSS diperoleh dari Copernicus Marine Environment Monitoring Service (CMEMS). Data tersebut memiliki resolusi spasial 1/12°, dan resolusi temporal harian. Data SSS selama Januari 2016 sampai dengan September 2021 diolah dengan analisis deret waktu. Penelitian ini menyajikan plot deret waktu, distribusi bulanan, sebaran spasial, dan transformasi wavelet kontinu (Continous Wavelet Transform, CWT).  Hasil penelitian ini menunjukkan bahwa nilai SSS berfluktuasi dengan kisaran nilai antara 31,66 – 34,77 practical salinity unit (psu) dan anomali berkisar antara -2,5 hingga 1,4 psu. Salinitas tertinggi terjadi pada bulan Juli dan terendah pada bulan Maret. Nilai SSS juga bervariasi secara spasial. Ketika memasuki musim timur, salinitas lebih tinggi masuk dari bagian timur dan bergerak ke barat. Namun, ketika mulai memasuki musim barat, salinitas lebih rendah masuk dari bagian barat dan bergerak ke timur. Variabilitas SSS menunjukkan adanya 3 periodisitas utama, yaitu variabilitas intra seasonal, variabilitas semi-anual, dan variabilitas anual.  Variabilitas anual mendominasi SSS di perairan selatan Kebumen.

Variabilitas salinitas permukaan laut perairan selatan kebumen

This paper aims to find the possible relationships between winter precipitation (December, January, February; DJF) in Iran with three oceanic sources through the correlation wavelet analysis by applying the continuous wavelet transform (CWT), the cross–wavelet transform (XWT), and the wavelet transform coherence (WTC). The sources in the North Atlantic Ocean (30°W-70°W, 10°N-30°N), the South Pacific Ocean (80°W-120°W, 20°S-40°S) and the Indian Ocean (50°E-100°E, 10°S-40°S) were selected using Pearson correlation coefficient (PCC > 0.5) that can represent the possible relationships between Iran’s winter precipitations with the oceanic sea surface temperature (SST) anomaly. The monthly gridded precipitation and SST data with a 2.5° × 2.5° resolution were evaluated from 1984 to 2019 to achieve this goal. The XWT results of precipitation and SST anomaly showed that the 8–16 months period is the most effective and predominant period between the South Pacific Ocean and 81% of all the precipitation zones. WTC results for the North Atlantic Ocean and 72% of all the precipitation zones showed periods of 4–8 (36%) and 16–32 (36%) months as the dominant duration. Despite the proximity of the Indian Ocean to the precipitation zones, there is no significant causal relationship between them, based on the XWT results. However, due to Madden–Julian oscillation (MJO), the 4–8 months period (45%) was seen between the Indian Ocean and some precipitation zones, based on WTC results.

Correlation Wavelet Analysis for Linkage between Winter Precipitation and Three Oceanic Sources in Iran

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Correlation Wavelet Analysis for Linkage between Winter Precipitation and Three Oceanic Sources in Iran