Series Of Meteorological Data Research Articles

Effects of Climate Change on Forest Regeneration in Central Spain

The Mediterranean climate has dry and hot summers, which is harsh for plants, especially seedlings. During the 1950s and 1960s, most reforestations carried out in Central Spain, a Mediterranean climate area, were successful, but in recent decades an increasing difficulty in forest regeneration has been observed, often attributed to increased summer drought. This study analyses changes in climatic parameters related to forest regeneration through statistical treatment of meteorological data series from the mid-twentieth century to the present. Simple and multiple regressions and ANOVAs were performed for five parameters, considering annual, summer and extended summer values. Rainfall reduction and prolongation of the summer drought period were not statistically significant. The change that better explains regeneration problems is the increase in temperature, especially in July and August, which was mostly significant between 2002 and 2021. Raising temperatures increase the vapor pressure deficit, exacerbating drought effects and plant mortality. Climate change scenarios point to an increase in temperatures until 2100; thus, the tipping point for natural regeneration of some species could be passed. The most affected species are those at their ecological limit. It is necessary to facilitate the adaptation of these forests to climate change, since their future will depend on the actions carried out today.

Millimeter slope ratcheting from multitemporal SAR interferometry with a correction of coastal tropospheric delay: A case study in Hong Kong

Tropospheric delays (TDs) limit the accurate detection of slow slope motion using interferometric synthetic aperture radar (InSAR), especially in subtropical coastal regions prone to frequent changes in humidity. Although TDs can be estimated through external weather data, their spatiotemporal resolution and data availability are greatly limited, which is not applicable for individual slopes. This paper presents new TD correction methods for slopes with both small dimensions and small elevation changes. We simultaneously estimated and eliminated the TD signal from a line-of-sight (LOS) time series through a blind source separation (i.e., independent component analysis). The stratified TD sources were isolated according to a spatially elevation-linked and temporally periodic independent-component (IC), which was determined via a correlation test and power spectrum analysis. Hence, the TD was corrected without the use of any external weather products/meteorological data and had unprecedented spatiotemporal details equivalent to the synthetic aperture radar (SAR) images. The proposed method was verified using CosmoSkyMed (CSK) and Sentinel-1 (SNT-1) images covering a slope in Tai O, Lantau Island, Hong Kong, and validated using a series of geodetic, meteorological, and hydrological data. Up to 3–4-cm relative TDs were measured in the LOS directions of CSK and SNT-1. The relative TD exhibited a slower increment rate than the slope elevation and was largely affected by specific air conditions (e.g., temperature and humidity) on the SAR image-acquisition days. The analysis of InSAR data yielded reasonably good estimates of millimeter-scale downslope slips (due to increases in pore-water pressure in the wet season) and upslope rebound (due to soil shrinkage in the dry season). It was found that soil swelling and shrinkage of the slope (also known as seasonal ratcheting) and the reclamation were likely regulated by rainfall and sea levels, respectively. Although the slope motion in Tai O was determined to be small (i.e., seasonal variations of 10 mm), the TD correction reduced the root-mean-square error by 42.3%, such that InSAR time-series measurements with millimeter-level accuracy (potentially 1–3 mm) were obtained.

Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation

With the rapid development of Artificial Intelligence (AI) technology, the Long Short-Term Memory (LSTM) network has been widely used for forecasting hydrological process. To evaluate the effect of training data amount on the performance of LSTM, the study proposed an experiment scheme. First, K-Nearest Neighbour (KNN) algorithm is employed for generating the meteorological data series of 130 years based on the observed data, and the Soil and Water Assessment Tool (SWAT) model is used to obtain the corresponding runoff series with the generated meteorological data series. Then, the 130 years of rainfall and runoff data is divided into two parts: the first 80 years of data for model training and the remaining 50 years of data for model verification. Finally, the LSTM models are developed and evaluated, with the first 5-year, 10-year, 20-year, 40-year and 80-year data series as training data respectively. The results obtained in Yalong River, Minjiang River and Jialing River show that increasing the training data amount can effectively reduce the over-fittings of LSTM network and improve the prediction accuracy and stability of LSTM network.

Analysis of two-decade meteorological and air quality trends in Rome (Italy)

This study presents a trend analysis of a two-decade (2000–2020) series of surface meteorological and air quality data measured in Rome (Italy). Data series are collected at three sites in Rome downtown and its coastal surroundings. This paper fills the gap due to missing or incomplete recent information about the meteorological and atmospheric composition tendencies in the Rome area. Datasets are subjected to in-depth quality control and to statistical analysis to verify the data homogeneity, whilst trend analysis is performed using the Seasonal Kendall test. The results show a statistically significant positive trend for average air temperature (0.07 °C year−1 in urban and coastal sites), whilst maximum and minimum temperatures increase more in urban (0.10 °C year−1) than in coastal (0.01 °C year−1) environment. The water vapour mixing ratio trend is higher in the city (0.10 g kg−1 year−1) than along the coast (0.03 g kg−1 year−1). The heat index tendency is more pronounced in the Rome centre (0.11 °C year−1) than in the coast (0.06 °C year−1). The monthly cumulative precipitations do not show statistically significant trends. On the other hand, air quality generally improved: surface pollutant concentrations show a significant decrease thanks to the reduction of local emissions (C6H6, − 0.12 µg m−3 year−1; SO2, − 0.09 µg m−3 year−1; CO, − 0.02 mg m−3 year−1; NOx, − 1.28 µg m−3 year−1; NO, − 0.38 µg m−3 year−1; NO2, − 0.60 µg m−3 year−1, PM10, − 0.35 µg m−3 year−1). Only O3 shows a statistically significant positive trend (0.15 µg m−3 year−1), in agreement with wider scale studies. The outcomes suggest that whilst local and national policies contribute to the improvement of air quality, the rising of temperature—as likely consequence of climate change—may be an emerging cause of concern for human thermal discomfort and potential effect on the conservation of tangible immovable heritage.

Prediction of Harvest Time of Apple Trees: An RNN-Based Approach

In the field of agricultural research, Machine Learning (ML) has been used to increase agricultural productivity and minimize its environmental impact, proving to be an essential technique to support decision making. Accurate harvest time prediction is a challenge for fruit production in a sustainable manner, which could eventually reduce food waste. Linear models have been used to estimate period duration; however, they present variability when used to estimate the chronological time of apple tree stages. This study proposes the PredHarv model, which is a machine learning model that uses Recurrent Neural Networks (RNN) to predict the start date of the apple harvest, given the weather conditions related to the temperature expected for the period. Predictions are made from the phenological phase of the beginning of flowering, using a multivariate approach, based on the time series of phenology and meteorological data. The computational model contributes to anticipating information about the harvest date, enabling the grower to better plan activities, avoiding costs, and consequently improving productivity. We developed a prototype of the model and performed experiments with real datasets from agricultural institutions. We evaluated the metrics, and the results obtained in evaluation scenarios demonstrate that the model is efficient, has good generalizability, and is capable of improving the accuracy of the prediction results.

Study of the Effect of Vegetation on Reducing Atmospheric Pollution Particles

Atmospheric particulate matter (PM) is a major air pollutant. PM2.5 and PM10 pose particularly serious threats to the ecological environment and human health. Vegetation plays an important role in reducing the concentration of particles. Based on a long time series of air quality, meteorological, and vegetation coverage data in the Beijing–Tianjin–Hebei (BTH) region, the present paper evaluated the influence at the overall and built-up area scales and quantified the process involved in the dry settlement of particles on vegetation based on a mathematical model. The experimental results showed that (1) the total amounts of PM10 reduced by vegetation in the BTH area were 505,200 t, 465,500 t, 477,200 t and 396,500 t in 2015, 2016, 2017 and 2018, respectively, and the total amount of PM2.5 was reduced by 19,400 t, 19,200 t, 16,400 t and 12,700 t, respectively. The annual reduction in PM10 and PM2.5 from 2015 to 2018 by vegetation in the BTH region showed a downwards trend, and the annual reduction was mainly caused by the significant decrease in PM concentration. (2) More than 80% of the reduction in annual yield was concentrated in May–September, and a large leaf area was the main reason for the largest yield reduction in the growing season. The efficiency of PM reduction in forestland was approximately five–seven times that in grassland, and the deciduous broad-leaved forest was the main driver of this reduction in each forest. (3) The reduction in PM10 by vegetation was approximately 30 times that of PM2.5. However, the reduction in PM2.5 by vegetation should not be ignored because PM2.5 has a stronger correlation with human production and living activities. Increasing the area and density of green space via afforestation, returning farmland to forest and giving full play to the self-purification function of green spaces are very important to reducing and controlling the concentration of PM.

Episodic extreme rainfall events drive groundwater recharge in arid zone environments of central Australia

Understanding the factors controlling groundwater recharge in arid vadose zones is critical for sustainable groundwater management in the face of climate variability and change. Here we estimated groundwater recharge in data sparse regions under arid climate by implementing a multi-model approach using HYDRUS-1D for a century-long time series of meteorological data and site-specific regolith hydraulic properties of a bare soil and a Mulga savanna-type soil in central Australia. Grain‐size analysis provided a contiguous 12-m deep profile of hydraulic properties that were generated by means of pedotransfer functions. To account for conceptual model uncertainty in hydraulic properties, eleven pedotransfer functions were applied. Climate data from three stations accounted for spatial heterogeneity in local climate of the Ti Tree Basin study area. Simulated water fluxes in the vadose zone indicated that only rainfall events of more than 150–200 mm resulted in groundwater recharge. Recharge was linked to extreme rainfall associated with monsoonal cyclones. Based on the 130-year climate record, average recharge for the savanna-type vegetation ranged from 4.3 to 7.4 mm/a across the three climate stations, with an overall mean of 4.6 mm/a. The bare soil had an overall mean recharge of 29.5 mm/a, ranging from 23.5 to 35.8 mm/a. Results from this study provide a better understanding of the highly episodic recharge in arid environments and are critical input to sustainably manage groundwater resources.

Prediction of the discharge in Pech River for the flood warning system, based on the time series of snow cover area and meteorological data

Prediction of the discharge in Pech River for the flood warning system, based on the time series of snow cover area and meteorological data

Prediction of the discharge in Pech River for the flood warning system, based on the time series of snow cover area and meteorological data

Prediction of the discharge in Pech River for the flood warning system, based on the time series of snow cover area and meteorological data

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.

Impact of ambient temperature on life loss per death from cardiovascular diseases: a multicenter study in central China

BackgroundIn the context of global climate change, studies have focused on the ambient temperature and mortality of cardiovascular diseases (CVDs). However, little is known about the effect of ambient temperature on year of life lost (YLL), especially the life loss per death caused by ambient temperature. In this study, we aimed to assess the relationship between ambient temperature and life loss and estimate the impact of ambient temperature on life loss per death.MethodsWe collected daily time series of mortality and meteorological data from 70 locations in Hunan province, central China, in periods ranging from Jan. 1, 2013, to Dec. 31, 2017. Crude rates of YLL were calculated per 100,000 people per year (YLL/100,000 population) for each location. A distributed lag nonlinear model and multivariate meta-regression were used to estimate the associations between ambient temperature and YLL rates. Then, the average life loss per death attributable to ambient temperature was calculated.ResultsThere were 711,484 CVD deaths recorded within the study period. The exposure-response curve between ambient temperature and YLL rates was inverted J or U-shaped. Relative to the minimum YLL rate temperature, the life loss risk of extreme cold temperature lasted for 10 to 12 days, whereas the risk of extreme hot temperature appeared immediately and lasted for 3 days. On average, the life loss per death attributable to non-optimum ambient temperatures was 1.89 (95% CI, 1.21-2.56) years. Life loss was mainly caused by cold temperature (1.13, 95% CI, 0.89‑1.37), particularly moderate cold (1.00, 95% CI, 0.78‑1.23). For demographic characteristics, the mean life loss per death was relatively higher for males (2.07, 95% CI, 1.44‑2.68) and younger populations (3.72, 95% CI, 2.06‑5.46) than for females (1.88, 95% CI, 1.21-2.57) and elderly people (1.69, 95% CI, 1.28-2.10), respectively.ConclusionsWe found that both cold and hot temperatures significantly aggravated premature death from CVDs. Our results indicated that the whole range of effects of ambient temperature on CVDs should be given attention.

Managing Vineyard Topography and Seasonal Variability to Improve Grape Quality and Vineyard Sustainability

Topography may induce variability in meteorological conditions at the meso-scale level and could influence grape quality. Understanding the impact of topography on grape ripening allows for the development of sustainable management practices that include topographical influences in their consideration. This is also important for applying proactive strategies able to counter the expected changes of climate. This study was conducted on cv. Nebbiolo vineyards in North-West Italy. The topographic traits were performed in 17 vineyards within the region, which had previously been identified as belonging to different terroir units. An analysis of historical meteorological data series was also carried out to characterize the units from the meteorological point of view. The grape composition was investigated during 2012 and 2013. Based on the topography traits, a Cluster Analysis classified the 17 vineyards into four groups. Differences among groups mainly concerned insolation and heat accumulation. Topography influenced the individual components of grape quality differently depending on the seasonal weather trend. Interactions between topography and vintage were observed for a few parameters. Better understanding the grapevine reaction to external factors/site characteristics can allow for improved site and season-specific management decision making and can contribute to improving vineyard sustainability while maintaining winery objectives and wine typicity.

Assessment of CHADFDM satellite‐based input dataset for the groundwater recharge estimation in arid and data scarce regions

AbstractAquifer natural recharge estimations are a prerequisite for understanding hydrologic systems and sustainable water resources management. As meteorological data series collection is difficult in arid and semiarid areas, satellite products have recently become an alternative for water resources studies. A daily groundwater recharge estimation in the NW part of the Lake Chad Basin, using a soil–plant‐atmosphere model (VisualBALAN), from ground‐ and satellite‐based meteorological input dataset for non‐irrigated and irrigated land and for the 2005–2014 period is presented. Average annual values were 284 mm and 30°C for precipitation and temperature in ground‐based gauge stations. For the satellite‐model‐based Lake Chad Basin Flood and Drought Monitor System platform (CHADFDM), average annual precipitation and temperature were 417 mm and 29°C, respectively. Uncertainties derived from satellite data measurement could account for the rainfall difference. The estimated mean annual aquifer recharge was always higher from satellite‐ than ground‐based data, with differences up to 46% for dryland and 23% in irrigated areas. Recharge response to rainfall events was very variable and results were very sensitive to: wilting point, field capacity and curve number for runoff estimation. Obtained results provide plausible recharge values beyond the uncertainty related to data input and modelling approach. This work prevents on the important deviations in recharge estimation from weighted‐ensemble satellite‐based data, informing in decision making to both stakeholders and policy makers.

Land degradation modeling of dust storm sources using MODIS and meteorological time series data

Land degradation affects environmental integrity and threatens sustainable development worldwide by contributing to a number of social, economic, and ecological problems. This study uses time series of MODIS products, meteorological data, and ground truth map from dust storm sources to assess the spatial-temporal behavior of land degradation through a Random Forest algorithm in the southwest of Iran. Spatial-temporal variations in land surface biophysical properties of dust sources and surrounding areas were modeled using different time-series data on a 16-days, monthly, seasonal, annual, and 20-year basis from 2000 to 2019. Land degradation was estimated using the Random Forest algorithm. Dust storm sources were used to evaluate land degradation estimates. Furthermore, sensitivity analysis was carried out to identify the most influential biophysical variables. The results revealed that (i) 16-day time series data performs best in assessing land degradation; (ii) Apparent Thermal Inertia (ATI) was identified as the most influential variable for land degradation assessment; and (iii) there is a marked tendency towards land degradation in areas with severe land cover changes, mainly change from wetland to other classes.

Prediction of Meteorological Parameter Time Series Data for the Forest Fire Early Warning System

A forest fire early warning system must be developed to reduce the impact of greater community losses. One effort to develop an early warning system is to use a forest fire hazard index as a potential assessment guide. The main factor which is a parameter in the fire hazard index calculation method is the meteorological parameter. In general, to know today’s fire hazard index is calculated from today’s weather conditions, but the need for an early warning system is to know the future fire hazard index. Based on a series of meteorological conditions data held for thirty-six months, using the backpropagation algorithm, it is estimated that the meteorological conditions will be several months to come. Several meteorological parameters have their respective roles, the unknown contribution of which is calculated. In this study, each parameter will be measured by predicting time series data and compared with the results of calculations. The method of calculating the forest fire index used is the McArthur Forest Fire Danger Index with the meteorological parameter elements are temperature, relative humidity, wind speed, and drought factor. Each parameter was trained in artificial neural networks and tested its predictions to produce accuracy for data series temperatures of 91.67%, the relative humidity of 83.33%, and wind speed of 50%.

Impact of climate change and human activities on the baseflow in a typical karst basin, Southwest China

Baseflow is an important component of river runoff and an important ecological indicator of environmental health. A quantitative analysis of the impact of climate change and human activities on baseflow based on a scientific and reasonable baseflow separation scheme is helpful for the maintenance of river ecological health and the rational allocation of water resources, especially in the karst basins of Southwest China. The major difficulties and challenges lie in how to select the best baseflow separation scheme for karst basins and how to quantitatively analyse the impacts of climate change and human activity on the baseflow value and the baseflow intra-annual distribution characteristics. Therefore, the original achievement and objective of this study were to further contribute to the literature to fill the gap in the knowledge of how to analyse the impacts of climate change and human activities on baseflow more reasonably and comprehensively in a karst basin. The proposed framework consists of a trend analysis of the precipitation, mean temperature and potential evapotranspiration, an ensemble method for selecting the optimal baseflow separation scheme, the climate elasticity method and the slope change ratio of the cumulative quantity method to quantitatively estimate the contributions from climate change and human activities to changes in baseflow values and baseflow intra-annual distribution characteristics. The methodology was successfully implemented using a 55-year time series (1963–2017) of meteorological data and flow data recorded by the stations in the Chengbi River Karst Basin (Southwest China). The results show that annual rainfall and annual potential evapotranspiration display an upward and downward trend, respectively, while the average annual temperature is relatively stable. The Chapman-Maxwell method with a parameter of 0.970 outperforms the other methods and is the best baseflow separation scheme. Human activities are estimated to be the main factors influencing the variation of baseflow values and the variation of the baseflow annual distribution characteristics, with a contribution rate of 65.89%–149.83%, and the contribution rate of climate change is −49.83%–34.11%.

Exploring the long-term vegetation dynamics of different ecological zones in the farming-pastoral ecotone in northern China.

The vegetation in the farming-pastoral ecotone in northern China is influenced by both natural and anthropogenic factors and has undergone drastic changes in the past decades. The farming-pastoral ecotone is the transition zone from agriculture to animal husbandry. The ecological environment of this ecotone is complex and fragile. Most researches have primarily focused on the entire farming-pastoral ecotone, seldomly considering the differences between different ecological zones characterized by soil, climate, and biome conditions. Based on the long time series of leaf area index (LAI) data, meteorological data, and land-use dataset, this study analyzed LAI variation trends, the correlations between LAI and climate factors, and the impact of land-use type change on vegetation in the farming-pastoral ecotone in northern China. Moreover, this paper makes a full study of the changes of the whole study area from the perspective of the differences between different ecological zones. The results showed that over 36years, areas with vegetation improvements were considerably larger than those with degradations. However, there were still 49.56% of the total area showing no significant vegetation change. There are differences in vegetation change and response to climate between the forest ecological zones and the grassland ecological zones. The vegetation improvement trends of the forest ecological zones were larger and more sensitive to temperature, while the vegetation improvements of the grassland ecological zones were relatively small, and were more sensitive to precipitation. Human activities promote LAI changes in areas close to the forest ecological zones. The change of land use indicates that the decrease of the overall natural vegetation area has not resulted in decreasing LAI. And there is a growing trend of woodland area in the grassland ecological zones. The study provides a theoretical basis for the management of the environment and vegetation in the farming-pastoral ecotone in northern China.

Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study

Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease.

Scenario Projections of Changes in Snow Water Equivalent Due to Possible Climate Changes in Different Regions of the Earth

The study was carried out under the international Earth System Model–Snow Model Intercomparison Project on ten experimental well-instrumented snow sites in different parts of the Earth and aimed to examine the ability of the SWAP land surface model, developed by the authors, to reproduce the current characteristics of snow cover dynamics. A procedure was developed for scenario projecting of changes in the characteristics of snow cover formation in the XXI century with the use of series of meteorological forcing data, calculated with the use of atmospheric and oceanic general circulation models. The developed procedure was used to evaluate changes in the characteristics of snow cover formation in the XXI century in the areas of snow site locations in the context of current climate changes.

Evaluation and Comparison of the Effectiveness Rate of the Various Meteorological Parameters on UNEP Aridity Index Using Backward Multiple Ridge Regression

Climate changes and its undesirable impacts (such as melting glaciers, the occurrence of floods, hurricanes and droughts, etc.) are among the most important disasters that human beings have faced in recent decades. Considering the important role of the meteorological parameters on climate change, in this research the effectiveness rate of various meteorological parameters including the mean minimum and maximum annual temperature (Max-Temp and Min-Temp), the mean annual temperature (M-Temp), the mean annual sunshine (Sunshine), the mean annual relative humidity (Humidity), the mean annual wind speed (Wind) and the mean of annual precipitation (Rainfall) on United Nations Environmental Programme (UNEP) aridity index was assessed and prioritized using the Backward Multiple Ridge Regression (BMRR). In this study, the meteorological data series of 25 synoptic stations in Iran with different climate conditions during 1967–2017 was used. The results indicated that the BMRR method had a nice capability to predict the UNEP index using the above-mentioned meteorological parameters (the linear regression between observed and predicted the UNEP index had no difference with perfect reliable line (Y = X) at 0.05 significant levels and the R2 between two mentioned data series were significant at 0.01 levels at all stations). According to the results, Rainfall, Wind and Max-Temp parameters were the most effective parameters on the UNEP aridity index, respectively and the Min-Temp, M-Temp and Sunshine parameters were the least effective parameters on the UNEP index, respectively. Therefore, it is suggested that all human activities that have direct or indirect effects in the increasing temperature and reducing the rainfall must be revised and optimized based on the principles of sustainable development.