Resource Management Planning Research Articles

Data mining tools for complex socio-economic processes and systems

The paper considers discovering new and potentially useful information from large amounts of data that actualizes the role of developing data mining tools for complex socio-economic processes and systems based on the principles of the digital economy and their processing using network applications. The stages of data mining for complex socio-economic processes and systems were outlined. The algorithm of data mining was considered. It is determined that the previously used stages of data mining, which were limited to the model-building process, can be extended through the use of more powerful computer technology and the emergence of free access to large amounts of multidimensional data. The available stages of data mining for complex socio-economic processes and systems include the processes of facilitating data preparation, evaluation, and visualization of models, as well as in-depth learning. The data mining tools for complex socio-economic processes and systems in the context of technological progress and following the big data paradigm were identified. The data processing cycle has been investigated; this process consists of a series of steps starting with the input of raw data and ending with the output of useful information. The knowledge obtained at the data processing stage is the basis for creating models of complex socio-economic processes and systems. Two types of models (descriptive and predictive) that could be created in the data mining process were outlined. Algorithms for estimating and analyzing data for modeling complex socio-economic processes and systems in accordance with the pre-set task were determined. The efficiency of introducing neural networks and deep learning methods used in data mining was analyzed. It was determined that they would allow effective analysis and use of the existing large data sets for operational human resources management and strategic planning of complex socio-economic processes and systems.

Landsat-Scale Regional Forest Canopy Height Mapping Using ICESat-2 Along-Track Heights: Case Study of Eastern Texas

Spaceborne profiling lidar missions such as the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) are collecting direct elevation measurements, supporting the retrieval of vegetation attributes such as canopy height that are crucial in forest carbon and ecological studies. However, such profiling lidar systems collect observations along predefined ground tracks which limit the spatially complete mapping of forest canopy height. We demonstrate that the fusion of ICESat-2 along-track canopy height estimates and ancillary Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) data can enable the generation of spatially complete canopy height data at a regional level in the United States. We developed gradient-boosted regression models relating canopy heights with ancillary data values and used them to predict canopy height in unobserved locations at a 30 m spatial resolution. Model performance varied (R2 = 0.44 − 0.50, MAE = 2.61–2.80 m) when individual (per month) Landsat data and LANDFIRE data were used. Improved performance was observed when combined Landsat and LANDFIRE data were used (R2 = 0.69, MAE = 2.09 m). We produced a gridded canopy height product over our study area in eastern Texas, which agreed moderately (R2 = 0.46, MAE = 4.38 m) with independent airborne lidar-derived canopy heights. Further, we conducted a comparative assessment with the Global Forest Canopy Height product, an existing 30 m spatial resolution canopy height product generated using GEDI (Global Ecosystem Dynamics Investigation) canopy height and multitemporal Landsat data. In general, our product showed better agreement with airborne lidar heights than the global dataset (R2 = 0.19 MAE = 5.83 m). Major differences in canopy height values between the two products are attributed to land cover changes, height metrics used (98th in this study vs 95th percentile), and the inherent differences in lidar sampling and their geolocation uncertainties between ICESat-2 and GEDI. On the whole, our integration of ICESat-2 data with ancillary datasets was effective for spatially complete canopy height mapping. For better modeling performance, we recommend the careful selection of ICESat-2 datasets to remove erroneous data and applying a series of Landsat data to account for phenological changes. The canopy height product provides a valuable spatially detailed and synoptic view of canopy heights over the study area, which would support various forestry and ecological assessments at an enhanced 30 Landsat spatial resolution.

Delineation of groundwater potential zones at micro-spatial units of Nagaon district in Assam, India, using GIS-based MCDA and AHP techniques.

Groundwater is one of the most precious naturalresources in the densely populated regions of the world. The ever increasing population in the developing countries like India and the concurrent growth of industrial and urban centres along with modern agricultural systems have caused tremendous pressure on groundwater. In the present day context of groundwater depletion and degradation, it is necessary to identify and map the groundwater potential zones (GWPZs) of an area to manage and utilize them sustainably. The present study is, therefore, an attempt to employ the GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP) techniques to derive the GWPZs in Nagaon district of the middle Brahmaputra valley agro-climatic region of Assam, India. Here, ten multi-influencing groundwater potential factors, including geology, lineament density, geomorphology, slope, soil texture, LULC, rainfall, drainage density, NDWI, and TWI are considered for the delineation ofGWPZs. These criteria are assigned weightages using AHP wherein geology has been given the highest weight, followed by lineament density and the others. Based on these techniques, the GWPZs of the district are classified into four zones, namely, low, moderate, high, and very high which include 20, 301, 218, and 392 villages, respectively. The results are validated with the groundwater levels of 28 tube wells and 5 ring wells distributed throughout the district, and as such, an area under the curve value of 86.9% is found. The results, thus, validated imply that the methodology adopted in deriving the GWPZs is highly reliable, which can help in sustainable utilization, development, and management of groundwater resources in the district. Besides, the identification and mapping of village-wise GWPZs are very essential for the planning of water resource management and water risks mitigation which ultimately help insustainable rural development.

Examining the Drivers of Land Use /Land Cover Change: A Case of the Kavango River Basin and its Immediate Environments in Namibia

Understanding the drivers of land cover changes (LULCC) is very crucial for the development of management strategies as well as policy improvement and the sustenance of ecosystem services. This is crucial in preventing further degradation and proper planning of sustainable natural resources management. In this study, an attempt has been made to identify the drivers of LULCC in the Kavango East and West Regions of Namibia, from 1990 to 2018. Remotely Sensed Images were used to compute indices. Socio-economic surveys were conducted using structured interviews to share the past experiences of the local people, some key informants, and other stakeholders in the region. A combination of this information together with the Remote Sensing data was then used to derive the drivers of LULCC in the study area. Results of the study showed that changes were triggered by the interplay of more than five drivers identified and related to the environment, socio-economic, and other technical factors. In particular, the establishment and expansions of agricultural land, settlement, urbanization, and lumbering (for timber and crafting) were viewed by local people as the leading cause of deforestation. Other factors such as drought, flooding, and lumbering (for construction and firewood) cannot be undermined. Future studies will be targeted at assessing these drivers to evaluate their impacts on achieving sustainable development in the Kavango River Basin of Namibia and its immediate environments.

Spatio-temporal variability and trends of hydroclimatic variables at Zarima Sub-Basin North Western Ethiopia

Globally, hydroclimatic variables have been changed due to human activities and have an impact on agricultural production and water resource management. This study investigated the spatio-temporal variability of hydroclimatic variables in Zarima subbasin using rainfall and temperature data from 1984 to 2018 and stream flow data from 1990 to 2014. The data were analyzed using the coefficient of variation, standardized anomaly index, Mann–Kendall trend and Sen’s slope estimator test. The results showed that rainfall, maximum and minimum temperatures of the Zarima subbasin varied spatially and temporally. The annual and Kiremit season rainfall and stream flow showed low to moderate variability (CV < 30%) whereas Bega and Belg season rain fall and stream flow showed high degree of variability (CV < 40%). The annual and Kiremit season rainfall showed relatively higher variability along the northern border of the subbasin. However, maximum temperature showed less degree of variability (CV < 10%) over the subbasin, whereas the annual and seasonal minimum temperatures showed less to moderate variability (CV = 2–28%). The minimum temperature showed moderate variability around the south east lower border of the subbasin on the annual time scale and the north east part of the subbasin during the Kiremit season. The Bega and Belg seasons mean minimum temperatures showed less to high degree of variability (CV = 0–40%). The south east lower part of the subbasin showed moderate to high variability during the Bega and Belg seasons. Results of a standardized anomaly index of the rainfall, temperature and stream flow confirmed that the sub basin experienced fluctuations between dry, wet, cool and warm years. The spatial representation of annual rainfall showed a statically significant increasing trend in some parts of the subbasin and an insignificant increasing trend in the majority of the subbasin up to 300 mm per decade, while the Bega and Belg seasons showed statistically insignificant increasing and decreasing trends. Kiremit season rainfall showed heterogenous results in both statistics and trend direction. The mean annual maximum showed an increasing trend from 0.35 to 0.9 °C. The Bega, Belg and Kiremit seasons' maximum temperatures increased by a range of 0.35 to 0.95 °C; 0.4 from 1.2 °C and 0.1 to 0.55 °C pre decade respectively. While the annual minimum temperature increased by 0.05 to 0.5 °C and decreasing trend by 0.1 to 0.3 °C around the south eastern part of the subbasin. The Bega and Belg season minimum temperatures increased up to 0.5 °C and 1 °C and temperatures decreased in the range of 0.1 to 0.5 oC per decade. The Kiremit season minimum temperature increased by a range of 0.05 to 0.5 °C. The annual, Bega, Belg and Kiremit season stream flow increased insignificantly by 0.32, 0.31, 0.02 and 0.68 mm3/s respectively. In general, the subbasin experienced hydroclimatic variability which affects the life of the people, agricultural production and the economy of the area. This needs further investigation for planning and management of water resource management for the success of future water resources projects.

Assessment of Water Quality of Tigris River and branch in Alkut City by using Water Quality Index (WQIA)

Our water quality continues to be negatively impacted by human activities, this is a global problem of critical importance (particularly concerning fresh water and human consumption). Since the 1960s, the critical water quality index (WQI) technique has been used to assess the worldwide water quality state of surface water and groundwater systems. Plans for water resource management must consider extensive data and knowledge about the quality of available water. Water quality indicators are a straightforward technical method for evaluating the state of a river's water quality. In this approach, many water quality characteristics are examined and interpreted in research on river water quality. It can be considered the most important parts of monitoring plans for river quality. In this study, a monitoring plan is achieved for three different stations located on Tigris and branch rivers Al-Dujaili and Al-Gharraf in Wasit/ Kut during the study period for eight weeks from 1/3/2022 to 1/5/2022. Water quality assessment has been conducted using arithmetic quality indices of general water used for drinking and agricultural consumption. It is where the qualitative indices are turned into a single number with no units. Classifying water quality is done by comparing the values ​​of the indices to a scale of ratings that have already been set up. In this study, It has been utilized the Water Quality Index. The following physical and chemical factors are used to determine the water quality index: pH, total dissolved solids (TDS), turbidity, biological oxygen demand (BOD5), nitrate (NO3), sulphate (SO4), chloride (Cl), and phosphate (PO4). The results showed that each station had a low rating for the water quality index The average readings for the Tigris River were 187.44, Al-Dujaili 211.49 and Al-Gharaf 255.85, showing that Tigris River and its branches' water is seriously polluted for aquatic life due to the discharge of insufficiently treated wastewater from Kut's residential neighbourhoods.

A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples

• Large-scale mapping methodology developed for complex wetlands. • High performances and transferability for large-scale wetland mapping. • The generation of synthetic Sentinel-1 and Sentinel-2 increases wetland classification accuracy. • Validated in three study areas in New Brunswick located in Canada through image interpretation and field data. Wetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of enough reference data for accurate large-scale wetland mapping. As such, the main objective of this study was to investigate the efficient deep-learning models for generating high-resolution and temporally rich training datasets for wetland mapping. The Sentinel-1 and Sentinel-2 satellites from the European Copernicus program deliver radar and optical data at a high temporal and spatial resolution. These Earth observations provide a unique source of information for more precise wetland mapping from space. The second objective was to investigate the efficiency of vision transformers for complex landscape mapping. As such, we proposed a 3D Generative Adversarial Network (3D GAN) to best achieve these two objectives of synthesizing training data and a Vision Transformer model for large-scale wetland classification. The proposed approach was tested in three different study areas of Saint John, Sussex, and Fredericton, New Brunswick, Canada. The results showed the ability of the 3D GAN to stimulate and increase the number of training data and, as a result, increase the accuracy of wetland classification. The quantitative results also demonstrated the capability of jointly using data augmentation, 3D GAN, and Vision Transformer models with overall accuracy, average accuracy, and Kappa index of 75.61%, 73.4%, and 71.87%, respectively, using a disjoint data sampling strategy. Therefore, the proposed deep learning method opens a new window for large-scale remote sensing wetland classification.

Working toward a National Coordinated Soil Moisture Monitoring Network: Vision, Progress, and Future Directions

Abstract Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&amp;D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

Length‐weight relationships of 12 dominant fish species from a Ramsar Wetland in India: Vembanad Lake

AbstractThe present study describes the length‐weight relationships (LWRs) of 12 dominant finfish species from Vembanad Lake, India, from June 2015 to May 2016. Samples were carried out using a wide range of fishing gears such as gill nets (mesh sizes 20–60 mm), seine nets (mesh sizes 8–16 mm), cast nets (mesh sizes 6–8 mm) and hook and lines. The values of the exponent b ranged from 2.506 (Glossogobius giuris) to 3.305 (Heteropneustes fossilis) and regression parameters R2 from .960 (Carinotetraodon travancoricus) to .995 (G. giuris). The study reports the new maximum total length (TLmax) for Hyporhamphus xanthopterus and first reference regarding length‐weight relationships for Mystus oculatus, Ompok malabaricus and C. travancoricus. The results provide useful information for preparing sustainable resource management plans and conservation strategies for these species in the lake and similar ecosystems.

Forecasting Potential Evapotranspiration Using Seasonal ARIMA Model for Northern Telangana Zone, India

Present study aims to predict the evapotranspiration values over the Northern Telangana Zone through the identification or patterns in correlated data trends and seasonal variation and to assess the accuracy of the forecasting model. Plans for managing crop water consumption include potential evapotranspiration heavily. As a result, in a semi-arid environment, forecasting of the potential evapotranspiration is the foundation of any successful water resources management plans. The Thronthwaite method was used to estimate daily evapotranspiration, and a Seasonal Auto Regressive Integrated Moving Average was used to forecast potential evapotranspiration. Time series analysis of evapotranspiration data set showed a seasonality behaviour and thus Seasonal ARIMA model with the least Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values were selected. The Seasonal Arima model selected for the districts Adilabad, Jagtial, Karimnagar, KumuramBheem, Nirmal, and Peddapalli was (2,0,2)(2,1,0)12 and for Nizamabad district (2,0,2)(1,1,0)12. Basic statistical properties are used to compare the observed and forecasted data which shown that that there is no significant difference between the mean values of the observed and predicted data at a 5% significance level. Hence the developed model was optimum to forecast the evapotranspiration over the study area and to sustain the forecasting accuracy.

Understanding Dry and Wet Conditions in the Vietnamese Mekong Delta Using Multiple Drought Indices: A Case Study in Ca Mau Province

Globally, hydrometeorological hazards have large impacts to agriculture output, as well as human well-being. With climate change derived increasing frequency of extreme weather conditions, the situation has becoming more severe. This study strives to evaluate both dry and wet conditions in the Vietnamese Mekong Delta (VMD), also known as the rice basket of the Southeast Asian region. Different meteorological parameters from the last three decades were used to develop drought indices for Ca Mau province to investigate their impact on agricultural output. For this purpose, the standard precipitation index (SPI), the agricultural rainfall index (ARI), and the standardized precipitation evapotranspiration index (SPEI) were used in this study. Results highlight that Ca Mau has a peculiar characteristic of the whole VMD in that dry periods persist well into the wet season extending the duration of drought events. The role of storms, including tropical storms, and El Niño cannot be ignored as extreme events, which both change humidity, as well as rainfall. It is also found that the drought situation has caused significant damage to both rice and shrimp outputs in almost 6000 hectares. The assessment contributes to an improved understanding of the pattern of unpredictable rainfall and meteorological anomaly conditions in Ca Mau. The findings of this paper are important for both policymakers and practitioners in designing more robust plans for water resource management.

Remote Sensing and GIS-aided Land and Water Resources Management Plan for a Prioritized Sub-watershed of the Kotni Watershed, Chhattisgarh, India

Land and water are the prime resources that support all forms of life through various processes. Integration of Remote Sensing and GIS is incredibly helpful in planning and management of this resources, it is superior to the limitation of typical land planning, causes it embraces the metaverse of planning and development of resources through modern technology. In the study area, these resources are in critical condition; approximately 6.5 % of land is agricultural fallow land, 3 % is barren land, and 50 % is current fallow in Rabi season caused by the majority of agricultural land being unproductive due to poor land and water resource management. This research makes prudent and effective use of the watershed resources to increase productivity and production in the study area. The runoff computation of the watershed was done using the SCS curve number approach for 11 years (2009-2019), it reveals average annual rainfall of 1329 mm, with 41% of runoff generation capacity, which is estimated as 483.95 Mm3 of generated runoff. Methodology also includes Multi-Criteria Decision Analysis (MCDA) techniques and weighted overlay analysis (WOA) which effectively uses various thematic maps to delineate suitable zones for water storage and harvesting structures. Finally, suitable sites for 7 check dams, 10 percolation tanks for groundwater recharging and 20 check dams, 61 farm ponds for water harvesting were identified and proposed in the study area. The research findings will help to conserve water and soil resources and may increase the agricultural area as well as the production and productivity of the respected field.

Assessment of meteorological drought in Balod district, India through GIScience and Remote sensing

The present study was conducted in Balod district to assess meteorological drought events for prevention and future water resource management planning. The study area has a deficit in rainfall with a higher rainfall variation. From 1990 to 2020, the rainfall anomaly index (RAI) was used to calculate meteorological drought and monitor dry and wet periods in the area. Rainfall datasets from IMD daily gridded data of 0.25º × 0.25º spatial resolution were combined with the Geographic information system (GIS) environment. The results from the index revealed that the highest negative values were observed in the years 2000, 2002, 2008 and 2009 which have faced severe drought events. The findings from the study can assist in making a drought preparedness plan to mitigate the risk with effective water resources management.

Wastewater reclamation trends in Thailand.

Thailand constantly faces the problem of water scarcity, resulting from an imbalance between available water supply and increasing water demand for economic and community expansion, as well as climate change. To address this shortage, wastewater reclamation is being planned and implemented throughout the country, along with a 20-year, long-term integrated water resource management plan. Significant opportunities from municipal wastewater treatment plants (WWTPs) are dependent on the following factors: the establishment of a reuse water framework and a tangible target for treated wastewater set by local government authorities; widespread recognition and adaptation of wastewater reuse measures in the agriculture, industry, tourism and service sectors regarding climate change and water stress; and the implementation of joint investment water reuse projects between private and government agencies. However, wastewater reclamation faces some significant challenges, specifically: the limitations of regulation and monitoring for specific reuse purposes; a lack of public confidence in the water quality; the limited commercial development of reclaimed wastewater research; and difficulties in self-sustaining business models through adapting circular economy principles. This study aims to provide an overview of the wastewater reclamation, present research trends, currently operating WWTPs as well as opportunities and challenges to speed up water reuse activities in Thailand.

Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study

In recent decades, natural calamities such as drought and flood have caused widespread economic and social damage. Climate change and rapid urbanization contribute to the occurrence of natural disasters. In addition, their destructive impact has been altered, posing significant challenges to the efficiency, equity, and sustainability of water resources allocation and management. Uncertainty estimation in hydrology is essential for water resources management. By quantifying the associated uncertainty of reliable hydrological forecasting, an efficient water resources management plan is obtained. Moreover, reliable forecasting provides significant future information to assist risk assessment. Currently, the majority of hydrological forecasts utilize deterministic approaches. Nevertheless, deterministic forecasting models cannot account for the intrinsic uncertainty of forecasted values. Using the Bayesian deep learning approach, this study developed a probabilistic forecasting model that covers the pertinent subproblem of univariate time series models for multi-step ahead daily streamflow forecasting to quantify epistemic and aleatory uncertainty. The new model implements Bayesian sampling in the Long short-term memory (LSTM) neural network by using variational inference to approximate the posterior distribution. The proposed method is verified with three case studies in the USA and three forecasting horizons. LSTM as a point forecasting neural network model and three probabilistic forecasting models, such as LSTM-BNN, BNN, and LSTM with Monte Carlo (MC) dropout (LSTM-MC), were applied for comparison with the proposed model. The results show that the proposed Bayesian long short-term memory (BLSTM) outperforms the other models in terms of forecasting reliability, sharpness, and overall performance. The results reveal that all probabilistic forecasting models outperformed the deterministic model with a lower RMSE value. Furthermore, the uncertainty estimation results show that BLSTM can handle data with higher variation and peak, particularly for long-term multi-step ahead streamflow forecasting, compared to other models.

Hydrological response to future climate change in a mountainous watershed in the Northeast of Tibetan Plateau

Study regionUpper reach of the Heihe River basin (UHRB) Study focusWe quantified the potential changes in future climate and streamflow, as well as their corresponding contribution to the annual variations. And the dominant factors of streamflow change were explored in further. Robust multivariate bias correction was applied to correct the outputs of the general circulation method (GCMs) before applying the climate projection. The Soil and Water Assessment Tool model was applied to explore streamflow characteristics under the climate projection of bias-corrected GCMs under four representative emission scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5). New hydrological insights for the regionBy the end of the 21st century, climate will follow a warming and wetting trend within the basin; the annual streamflow is projected to increase by > 18 % in the 2030 s, and > 36 % in the 2050 s and 2090 s. Overall, the minimum temperature had a positive impact on streamflow in the cold seasons, especially in spring, contributing the most to the annual streamflow increment. When the temperature increase is greater than 5.42 °C, the peak streamflow will shift forward from May to April. Moreover, evapotranspiration will increase with temperature, leading to a decrease in summer streamflow. The results enable us to better understand the climate and streamflow changes at different time scales and provide guidance for the development of climate change response strategies and water resource management planning.

Hydrological Drought Forecasting Using a Deep Transformer Model

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: MSE=0.11, MAE=0.21, RSME=0.31, and R2=0.92 for the Chattahoochee station, and MSE=0.06, MAE=0.19, RSME=0.23, and R2=0.93 for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere.

Calculation of Potential Groundwater Resources in Orkhon River Basin

Available groundwater research work and data levels are very limited due to the wide territory of our country. Potential Groundwater resources need to be calculated by basin to implement Integrated Water Resources Management Plans. For this purpose, previously the calculations were done twice in the Orkhon River Basin. The last calculation was done 10 years ago, therefore Integrated Water Resources Management Plan needs to be redeveloped, using updated research work and data levels. The Orkhon River is one of the biggest rivers in Mongolia, rising from Khangai Mountain Range and flowing into the Selenge River. The Orkhon River Basin includes the Tuul, Kharaa, and Yeruu rivers basins which rise in the Khentii Mountains. For the first time, a hydrogeological structure map of the basin was developed which analyzed and systemized the hydrogeological structure, the morphostructure factors, and the hydraulic properties of the hydrogeological formations distributed in the Orkhon River Basin. There are two types of hydrogeological basins and three types of hydrogeological massifs in the basin. This paper presents the calculation of the Potential Groundwater Resources (PGR) in the Orkhon River Basin based on groundwater well data, reports of groundwater exploration and research works, and the hydrogeological maps at a scale of 1:500,000. As a result of this calculation, there are 1.99 km3 of PGR in this basin.The largest resources are found in the linear intermountain hydrogeological basin which occupies 98 percent of the total PGR. Further improvement of the quality and standards of water research remains an important issue for hydrogeologists today.

Natural resource management in American national parks: Principles, problems and enlightenment

The natural resources of national parks in the United States are mainly divided into eight categories: biological resources, fire resources, water resources, air resources, geological resources, soundscape, lighting, chemicals and odors. The management procedures set by the National Park Administration include the formulation of natural resource management planning, the preservation and publication of natural resource information, the assessment of the impact of natural resources, the establishment of the extensive cooperative relations, the promotion of the restoration of natural ecosystems and the establishment of natural resources damage compensation system etc. Different management principles and methods are adopted for different types of natural resources. The “application–review–evaluation–permission” procedure for the development and utilization of natural resources is established and strictly implemented. However, there are also some problems, such as insufficient coverage of natural resources, dislocation of unified management and decentralized management, and serious shortage of financial investment. China’s national parks should clearly define the types of natural resources, gradually expand the pilot scope of natural resource asset management system, establish and improve the natural resource management system, highlight the integrity of ecosystem, adhere to management according to law, and strengthen the control of land and space use.

A mixed-integer optimization model for water sector planning and policy making in arid regions

Water sector planning and policy making in arid and semi-arid regions are challenging because many drivers and decision criteria require consideration. In this study, a multi-period mixed-integer linear programming model was developed to integrate and economically evaluate water management options for water supply in arid regions. The applicability of the proposed approach was demonstrated through a case study in the Emirate of Abu Dhabi (EAD), United Arab Emirates. The model was programmed in general algebraic modeling system (GAMS) and solved using the Cplex solver. The model determined the optimal economic and environmental costs, capacity expansion of treatment plants and water transmission systems, and other environmental aspects including the carbon footprint and brine discharge. Results show that the capacity contribution of reverse osmosis for desalination is expected to increase from 5.1% in 2021 to 18.1% in 2050. Based on the model's results for the case study, it was concluded that even with moderate consideration of environmental aspects, desalination plants in the EAD need a major technology transformation from thermal desalination to reverse osmosis The proposed model is proved to be effective for integrated water resources management and infrastructure planning in the EAD, and has the potential for effective application in other arid or semi-arid countries.