Muhammad Bilal Ahmad, Ali Muavia, Mudassar Iqbal, Abu Bakar Arshed, Muhammad Mansoor Ahmad
Abstract Climatic variations cause droughts which badly affect the environment. The study focused on monitoring droughts to aid decision-making and mitigate their negative impacts on water availability for agriculture and livelihoods in the face of increasing water demand and climate change. To assess the agricultural droughts, a new agricultural Standardized Precipitation Index (aSPI) was calculated which is not used earlier in Balochistan. Widely recommended Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used for meteorological drought assessment. Drought indices comparison was also conducted to check the applicability. Rainfall, maximum temperature, and minimum temperature data (1992 to 2021) were utilized to calculate SPI, aSPI, and SPEI at different timescales (3, 6, 9, and 12 months) using DrinC software and SPEI calculator. Indices results revealed the following severe to extreme drought years: 1998, 1999, 2000, 2001, 2002, 2004, 2008, 2011, 2014, 2016, and 2017. It was determined that Dalbandin, Quetta, Sibi, Kalat, Khuzdar, and Zhob experienced higher extreme drought frequencies. Both long- and short-term drought durations were observed. Indices comparison showed that SPI is the most efficient drought index compared to aSPI and SPEI. This study offers valuable insights for managing water resources in the face of climate-induced droughts.
{"title":"Spatio-temporal drought assessment and comparison of drought indices under climatic variations in drought-prone areas of Pakistan","authors":"Muhammad Bilal Ahmad, Ali Muavia, Mudassar Iqbal, Abu Bakar Arshed, Muhammad Mansoor Ahmad","doi":"10.2166/wcc.2023.602","DOIUrl":"https://doi.org/10.2166/wcc.2023.602","url":null,"abstract":"Abstract Climatic variations cause droughts which badly affect the environment. The study focused on monitoring droughts to aid decision-making and mitigate their negative impacts on water availability for agriculture and livelihoods in the face of increasing water demand and climate change. To assess the agricultural droughts, a new agricultural Standardized Precipitation Index (aSPI) was calculated which is not used earlier in Balochistan. Widely recommended Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used for meteorological drought assessment. Drought indices comparison was also conducted to check the applicability. Rainfall, maximum temperature, and minimum temperature data (1992 to 2021) were utilized to calculate SPI, aSPI, and SPEI at different timescales (3, 6, 9, and 12 months) using DrinC software and SPEI calculator. Indices results revealed the following severe to extreme drought years: 1998, 1999, 2000, 2001, 2002, 2004, 2008, 2011, 2014, 2016, and 2017. It was determined that Dalbandin, Quetta, Sibi, Kalat, Khuzdar, and Zhob experienced higher extreme drought frequencies. Both long- and short-term drought durations were observed. Indices comparison showed that SPI is the most efficient drought index compared to aSPI and SPEI. This study offers valuable insights for managing water resources in the face of climate-induced droughts.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the Gene Expression Programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972–2017) were used. Out of this statistical period, 40 years was used for calibration, and five years (1995–1999) for the validation of the model results. The results showed that the GEP model with an average R2 value of 0.948, average RMSE value of 19.4 m3/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R2 value of 0.04, average RMSE value of 298.2 m3/s, average NSE value of −0.64, and average SE value of 4.6 in predicting monthly runoff.
{"title":"Comparison of the performances of the gene expression programming model and the RegCM model in predicting monthly runoff","authors":"Sajjad Pouyanfar, Hamed Nozari, Mehraneh Khodamoradpour","doi":"10.2166/wcc.2023.439","DOIUrl":"https://doi.org/10.2166/wcc.2023.439","url":null,"abstract":"Abstract Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the Gene Expression Programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972–2017) were used. Out of this statistical period, 40 years was used for calibration, and five years (1995–1999) for the validation of the model results. The results showed that the GEP model with an average R2 value of 0.948, average RMSE value of 19.4 m3/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R2 value of 0.04, average RMSE value of 298.2 m3/s, average NSE value of −0.64, and average SE value of 4.6 in predicting monthly runoff.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Scientific and effective urban waterlogging risk prediction can help improve urban waterlogging disaster prevention capabilities. Combining the numerical simulation model with the data-driven model, the construction of the urban waterlogging risk predictive model can satisfy the prediction accuracy and improve the prediction timeliness. Thus, this paper established an urban waterlogging risk predictive model based on the coupling of the BP neural network and SWMM model, and set five input patterns, finally selected the accumulative precipitation process and precipitation characteristics as input to predict the regional waterlogging risks under different urban rainstorm scenarios. The results show that the overall performance of the pipe drainage system in the study area is lower, and it cannot resist the rainstorm with a higher return period. Moreover, the total waterlogging risk of the southern old city is higher than that of the northern new city in the study area. The calculation speed of the prediction model constructed in this paper is thousands of times higher than that of the numerical model, so the calculation speed is very fast, which meets the requirements of the forecast timeliness.
{"title":"Research on urban waterlogging risk prediction based on the coupling of the BP neural network and SWMM model","authors":"Jinping Zhang, Xuechun Li, Haorui Zhang","doi":"10.2166/wcc.2023.076","DOIUrl":"https://doi.org/10.2166/wcc.2023.076","url":null,"abstract":"Abstract Scientific and effective urban waterlogging risk prediction can help improve urban waterlogging disaster prevention capabilities. Combining the numerical simulation model with the data-driven model, the construction of the urban waterlogging risk predictive model can satisfy the prediction accuracy and improve the prediction timeliness. Thus, this paper established an urban waterlogging risk predictive model based on the coupling of the BP neural network and SWMM model, and set five input patterns, finally selected the accumulative precipitation process and precipitation characteristics as input to predict the regional waterlogging risks under different urban rainstorm scenarios. The results show that the overall performance of the pipe drainage system in the study area is lower, and it cannot resist the rainstorm with a higher return period. Moreover, the total waterlogging risk of the southern old city is higher than that of the northern new city in the study area. The calculation speed of the prediction model constructed in this paper is thousands of times higher than that of the numerical model, so the calculation speed is very fast, which meets the requirements of the forecast timeliness.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136136879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kashif Jamal, Xin Li, Yingying Chen, Sajjad Haider, Muhammad Rizwan, Shakil Ahmad
Abstract Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.
{"title":"Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya","authors":"Kashif Jamal, Xin Li, Yingying Chen, Sajjad Haider, Muhammad Rizwan, Shakil Ahmad","doi":"10.2166/wcc.2023.202","DOIUrl":"https://doi.org/10.2166/wcc.2023.202","url":null,"abstract":"Abstract Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136238323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nikita Garg, Srishti Negi, Ridhima Nagar, Shruthi Rao, Seeja K. R.
Abstract Flood is India's most prevalent natural calamity, devastatingly affecting human lives, infrastructure, and agriculture. Predicting floods can help to mitigate the potential damage and conduct timely evacuation drives. This research proposes a deep-learning regression model to forecast flood runoff. Various climatological, hydrological, land, and vegetation-related data have been collected from multiple sources for 18 years (2002–2019) to create a comprehensive dataset for the Godavari River at the Perur water station in India. The relevant attributes identified through feature selection are river water level, precipitation, temperature, surface pressure, evaporation, soil water content, daily runoff, and average river flow. The selected features were fed into various time series prediction models like AutoRegressive Integrated Moving Average (ARIMA), Prophet, Neural Prophet, and Long Short-Term Memory (LSTM). The LSTM model obtained the best results achieving a Root Mean Squared Error (RMSE) value of 0.05, Mean Absolute Error (MAE) value of 0.007, Willmott's Index (WI) of 0.83, Legates-McCabe's Index (LMI) of 0.58, and R2 of 0.67 for a 1-day prediction with a look-back window of 183 days. The model is also trained to predict the flood runoff value for a week ahead. The proposed model can serve as an essential component in flood warning systems.
洪水是印度最常见的自然灾害，对人类生活、基础设施和农业造成了毁灭性的影响。预测洪水可以帮助减轻潜在的损失，并及时进行疏散。本研究提出一种深度学习回归模型来预测洪水径流。从多个来源收集了18年(2002-2019)的各种气候、水文、土地和植被相关数据，为印度Perur水站的戈达瓦里河创建了一个综合数据集。通过特征选择确定的相关属性包括河流水位、降水、温度、地表压力、蒸发、土壤含水量、日径流量和平均河流流量。选择的特征被输入到各种时间序列预测模型中，如自回归综合移动平均(ARIMA)、先知、神经先知和长短期记忆(LSTM)。LSTM模型的预测结果最好，RMSE为0.05,MAE为0.007,Willmott's Index (WI)为0.83,legats - mccabe 's Index (LMI)为0.58,R2为0.67，回顾窗口为183天。该模型还经过训练，可以预测未来一周的洪水径流量。该模型可作为洪水预警系统的重要组成部分。
{"title":"Multivariate multi-step LSTM model for flood runoff prediction: a case study on the Godavari River Basin in India","authors":"Nikita Garg, Srishti Negi, Ridhima Nagar, Shruthi Rao, Seeja K. R.","doi":"10.2166/wcc.2023.374","DOIUrl":"https://doi.org/10.2166/wcc.2023.374","url":null,"abstract":"Abstract Flood is India's most prevalent natural calamity, devastatingly affecting human lives, infrastructure, and agriculture. Predicting floods can help to mitigate the potential damage and conduct timely evacuation drives. This research proposes a deep-learning regression model to forecast flood runoff. Various climatological, hydrological, land, and vegetation-related data have been collected from multiple sources for 18 years (2002–2019) to create a comprehensive dataset for the Godavari River at the Perur water station in India. The relevant attributes identified through feature selection are river water level, precipitation, temperature, surface pressure, evaporation, soil water content, daily runoff, and average river flow. The selected features were fed into various time series prediction models like AutoRegressive Integrated Moving Average (ARIMA), Prophet, Neural Prophet, and Long Short-Term Memory (LSTM). The LSTM model obtained the best results achieving a Root Mean Squared Error (RMSE) value of 0.05, Mean Absolute Error (MAE) value of 0.007, Willmott's Index (WI) of 0.83, Legates-McCabe's Index (LMI) of 0.58, and R2 of 0.67 for a 1-day prediction with a look-back window of 183 days. The model is also trained to predict the flood runoff value for a week ahead. The proposed model can serve as an essential component in flood warning systems.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Namran F. Hamad, Basim K. Nile, Hassan Thoulfikar A. Alamir, Ahmed M. Faris, Hani K. Ismail, Waqed H. Hassan, Luma M. Ahmed, Hasan Fisal Alesary, Stephen Barton
Abstract H2S is one of the principal odor gases released from sewer networks and understanding the rate of H2S release into sewer air space and ventilation to the atmosphere is crucial for preventing or minimizing odor and corrosion issues in sewer systems. TOXCHEM model was used to simulate the fate of H2S gas in roads for this study. The model was calibrated for the spring and summer seasons and validated for the remainder of the seasons. The predicted behavior showed good correlation to measurements on real samples following statistical analysis, with R2, R, and RMSE results between (0.93–0.97), (0.8–0.82), and (0.000438–0.000838), respectively. A sensitivity study was performed to assess the effect of various pH values, drop heights, tailwater depths, stream widths, and sewer ventilation rate levels. The results showed that the emissions concentrations for winter, spring, summer, and autumn reached 3500, 5044, 6425, and 4045 ppm respectively. All the emissions levels from this DS can be considered hazardous, and this was particularly evident during the summer months. This study has helped to clarify the fate and emission of hydrogen sulfide gas at the DS by simulation using a TOXCHEM model.
{"title":"Case study of hydrogen sulfide release in the sulfate-rich sewage drop structure","authors":"Namran F. Hamad, Basim K. Nile, Hassan Thoulfikar A. Alamir, Ahmed M. Faris, Hani K. Ismail, Waqed H. Hassan, Luma M. Ahmed, Hasan Fisal Alesary, Stephen Barton","doi":"10.2166/wcc.2023.283","DOIUrl":"https://doi.org/10.2166/wcc.2023.283","url":null,"abstract":"Abstract H2S is one of the principal odor gases released from sewer networks and understanding the rate of H2S release into sewer air space and ventilation to the atmosphere is crucial for preventing or minimizing odor and corrosion issues in sewer systems. TOXCHEM model was used to simulate the fate of H2S gas in roads for this study. The model was calibrated for the spring and summer seasons and validated for the remainder of the seasons. The predicted behavior showed good correlation to measurements on real samples following statistical analysis, with R2, R, and RMSE results between (0.93–0.97), (0.8–0.82), and (0.000438–0.000838), respectively. A sensitivity study was performed to assess the effect of various pH values, drop heights, tailwater depths, stream widths, and sewer ventilation rate levels. The results showed that the emissions concentrations for winter, spring, summer, and autumn reached 3500, 5044, 6425, and 4045 ppm respectively. All the emissions levels from this DS can be considered hazardous, and this was particularly evident during the summer months. This study has helped to clarify the fate and emission of hydrogen sulfide gas at the DS by simulation using a TOXCHEM model.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Various inventions are presented in the water industry, essential for the water supply, distribution, treatment, storage, and consumption optimization. It is necessary to confirm their novelty, innovative steps, and industrial applicability to protect water-related patents. However, the impacts of climate change are not considered in the granting of patents, and water-related inventions are registered or rejected regardless of these impacts. For example, an invention that causes greenhouse gas emissions may be patented because it is new. This research addresses this significant challenge using a descriptive-analytical approach and a library-field method. Based on the results, it is necessary to impose strictness on inventions that aggravate climate change (29% of the inventions investigated) and protect inventions that adapt to climate change impacts (71%). Furthermore, it is possible to use the tool of compulsory licensing to adapt to climate change and reduce its negative impacts. Moreover, the patent offices should evaluate climate change impacts by examining innovative steps and industrial applications. An invention that has negative impacts will be deprived of patent protection and considered one of the limitations and exceptions. Also, it is necessary to provide new interpretations of protection elements of the patent system.
{"title":"Analysis of impacts of climate change on the grant and protection of patents related to the water industry","authors":"Hossein Shakeri, Zahra Shakeri","doi":"10.2166/wcc.2023.376","DOIUrl":"https://doi.org/10.2166/wcc.2023.376","url":null,"abstract":"Abstract Various inventions are presented in the water industry, essential for the water supply, distribution, treatment, storage, and consumption optimization. It is necessary to confirm their novelty, innovative steps, and industrial applicability to protect water-related patents. However, the impacts of climate change are not considered in the granting of patents, and water-related inventions are registered or rejected regardless of these impacts. For example, an invention that causes greenhouse gas emissions may be patented because it is new. This research addresses this significant challenge using a descriptive-analytical approach and a library-field method. Based on the results, it is necessary to impose strictness on inventions that aggravate climate change (29% of the inventions investigated) and protect inventions that adapt to climate change impacts (71%). Furthermore, it is possible to use the tool of compulsory licensing to adapt to climate change and reduce its negative impacts. Moreover, the patent offices should evaluate climate change impacts by examining innovative steps and industrial applications. An invention that has negative impacts will be deprived of patent protection and considered one of the limitations and exceptions. Also, it is necessary to provide new interpretations of protection elements of the patent system.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marulak Pardede, Mosgan Situmorang, Syprianus Aristeus, Ismail Rumadan, Henry Donald Lumban Toruan, None Diogenes, None Djamilus, Ellen Lutya Putri Nugrahani
Abstract This study uses a normative juridical method based on literature studies to obtain secondary data sourced from primary, secondary, and tertiary legal materials. Specifications of research are analytically descriptive. The data analysis method used is qualitative juridical. The results of the study show that overall government policies on environmentally friendly management have not been able to overcome the conflicts that often arise between the goals of environmental preservation and the goals of economic development. As a solution to overcome these obstacles, it is necessary to enforce criminal law, in addition to imposing material punishments (requiring proof) for crimes which are genetic crimes, it is also necessary to apply formal offenses (no need for proof) for crimes which are specific crimes. The process of enforcing environmental law from the aspect of criminal law will be more successful if it is handled by agencies that technically and institutionally deal with environmental issues. In addition, the concept of sustainable development must be implemented in the legal system for environmental management. In the future, it is necessary to develop coordination between law enforcement officials who are assembled.
{"title":"Perspectives of sustainable development vs. law enforcement on damage, pollution and environmental conservation management in Indonesia","authors":"Marulak Pardede, Mosgan Situmorang, Syprianus Aristeus, Ismail Rumadan, Henry Donald Lumban Toruan, None Diogenes, None Djamilus, Ellen Lutya Putri Nugrahani","doi":"10.2166/wcc.2023.417","DOIUrl":"https://doi.org/10.2166/wcc.2023.417","url":null,"abstract":"Abstract This study uses a normative juridical method based on literature studies to obtain secondary data sourced from primary, secondary, and tertiary legal materials. Specifications of research are analytically descriptive. The data analysis method used is qualitative juridical. The results of the study show that overall government policies on environmentally friendly management have not been able to overcome the conflicts that often arise between the goals of environmental preservation and the goals of economic development. As a solution to overcome these obstacles, it is necessary to enforce criminal law, in addition to imposing material punishments (requiring proof) for crimes which are genetic crimes, it is also necessary to apply formal offenses (no need for proof) for crimes which are specific crimes. The process of enforcing environmental law from the aspect of criminal law will be more successful if it is handled by agencies that technically and institutionally deal with environmental issues. In addition, the concept of sustainable development must be implemented in the legal system for environmental management. In the future, it is necessary to develop coordination between law enforcement officials who are assembled.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135015006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Natural fluctuation of the hydrological regime is the key to maintaining river ecosystem function. Given the shortcomings of previous studies on hydrological regime change and the ecological response of the Min River, this study combined two change degree evaluation methods and Budyko theory to quantify the degree of ecohydrological change and its driving factors. Ecological significance indicators (ecosurplus and ecodeficit) and the Shannon index (SI) were used to identify the characteristics of ecohydrological variation and ecological response mechanisms. The results showed the following: (1) The hydrological regime in the Min River basin had an abrupt change in 1993, with the overall alteration degree reaching 44%, which further led to a decrease in ecological surplus and an increase in the ecological deficit in ecological indicators. (2) Budyko's theoretical results show that climate change and human activities together lead to an 83.83 mm reduction in Min River runoff, with human activities contributing 54.20% of the change in the mean annual runoff, while rainfall and evapotranspiration contribute 43.88 and 1.92%, respectively. (3) The SI index indicates a decreasing trend in Min flow biodiversity. The results of the study can provide a reference for enhancing ecological protection and restoration in the Min River basin.
{"title":"Analysis of hydrological regime evolution and ecological response in the Min River, China","authors":"Hongxiang Wang, Baoliang Wang, Huan Yang, Hongtong Zhou, Hao Chen, Wenxian Guo","doi":"10.2166/wcc.2023.210","DOIUrl":"https://doi.org/10.2166/wcc.2023.210","url":null,"abstract":"Abstract Natural fluctuation of the hydrological regime is the key to maintaining river ecosystem function. Given the shortcomings of previous studies on hydrological regime change and the ecological response of the Min River, this study combined two change degree evaluation methods and Budyko theory to quantify the degree of ecohydrological change and its driving factors. Ecological significance indicators (ecosurplus and ecodeficit) and the Shannon index (SI) were used to identify the characteristics of ecohydrological variation and ecological response mechanisms. The results showed the following: (1) The hydrological regime in the Min River basin had an abrupt change in 1993, with the overall alteration degree reaching 44%, which further led to a decrease in ecological surplus and an increase in the ecological deficit in ecological indicators. (2) Budyko's theoretical results show that climate change and human activities together lead to an 83.83 mm reduction in Min River runoff, with human activities contributing 54.20% of the change in the mean annual runoff, while rainfall and evapotranspiration contribute 43.88 and 1.92%, respectively. (3) The SI index indicates a decreasing trend in Min flow biodiversity. The results of the study can provide a reference for enhancing ecological protection and restoration in the Min River basin.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, significant changes are noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 (pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.
{"title":"Assessing seasonal variation and trends in rainfall patterns of Madhya Pradesh, Central India","authors":"Amit Kumar, Siddharth Kumar, Kuldeep Singh Rautela, Sulochana Shekhar, Tapas Ray, Mohanasundari Thangavel","doi":"10.2166/wcc.2023.280","DOIUrl":"https://doi.org/10.2166/wcc.2023.280","url":null,"abstract":"Abstract Climate change is a worldwide problem caused by various anthropogenic activities, leading to changes in hydroclimatic variables like temperature, rainfall, riverine flow, and extreme hydrometeorological events. In India, significant changes are noted in its natural resources and agriculture sectors. In this study, we analysed the long-term spatio-temporal change in rainfall patterns of Madhya Pradesh, Central India, using Indian Meteorological Department high-resolution gridded data from 439 grid points. The coefficient of variance analysis showed low variability in annual and monsoon rainfall but significant variability in pre-monsoon, post-monsoon, and winter seasons, indicating considerable seasonal variation. Pre-monsoon rainfall exhibited an increasing trend (0.018 mm annually), while annual, monsoon, post-monsoon, and winter rainfall showed decreasing trends. Change point analysis identified shifts in rainfall patterns in 1998 (monsoon, annual), 1955 (pre-monsoon), 1987 (post-monsoon), and 1986 (winter). Spatio-temporal distribution maps depicted irregular rainfall, with some areas experiencing drastic declines in precipitation after 1998. The maximum average annual rainfall reduced from 1,769 to 1,401 mm after 1998 affecting water availability. The study's findings highlight a significant shift in Madhya Pradesh's seasonal rainfall distribution after 1998, urging researchers and policymakers to address water-intensive cropping practices and foster climate resilience for a sustainable future in the region.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135551972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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