Validation Period Research Articles

Modeling rainfall-runoff estimation and assessing water harvesting zone for irrigation practices in Keleta watershed, Awash river basin, Ethiopia

The world’s water demand is increasing twice as quickly as the world’s population. Contrarily, the greatest problem facing humanity in the twenty-first century is the supply of food. During dry seasons, when water collecting is a crucial survival strategy, the communities in the study area experience water deficit for agricultural production. In this study, socioeconomic and biophysical parameters were considered to evaluate the water harvesting zone for irrigation practices. With the aid of this technology, water harvesting sites can be found in acceptable sites, and water resource management can be improved to address water shortages during the dry season when crops are at key stages of growth and filling. The integrated geographical information system/Remote Sensing (GIS/RS), Soil Water Assessment Tool (SWAT), fuzzy logic, and analytic hierarchy process (AHP) model were used to carry this out. Rainfall, drainage density, surface runoff, percentage of clay content, land use land cover, slope, lithology, lineament, and Euclidian distance to settlement and road were the criteria utilized to choose the Rainwater Harvesting (RWH) sites. The run-off was estimated using the SWAT model, weights for each influential component were determined using AHP, the input factor was standardized using fuzzy membership, and factors were combined using gamma fuzzy (ℽ= 0.9) overlay weighting. The model’s statistical performance was demonstrated by R2 values of 0.79 and NSE values of 0.77 for monthly calibration periods and R2 values of 0.81 and NSE values of 0.75 for monthly validation periods. The study area’s Rainwater Harvesting potential suitability classes covered very highly suitable (16.91%), extremely suitable (27.6%), moderately suitable (20.23%), low suitable (13.1%), and not suitable (6.43%), and restrictions (15.73%). The study’s findings could help decision-makers identify more lucrative and long-lasting irrigation investment options in the Keleta watershed and make better decisions regarding the development of irrigation projects there.

Reliability-weighted approach for streamflow prediction at ungauged catchments

Understanding the hydrological processes and the determination of the frequencies and magnitudes of streamflow are crucial for better oversight of available water resources. A significant obstacle to accurately assessing the water resources of a basin is the accessibility of data for hydrologic modeling. For example, nearly 70% of Ethiopia's Omo-Gibe River basin is ungauged, despite the fact that it is currently undergoing major socioeconomic activity, including the development of cascade hydropower generation. As a result, regionalization remains a critical difficulty in creating solid projections in these ungauged catchments. Thus, this study aimed to provide a dependable technique for accurately determining surface water resources in ungauged catchments. To accomplish this, a new approach called Reliability-weighted (RB) has been introduced to predict runoff in regions with unreliable data. This method combines the advantages of three commonly used parameter transfer methods: global mean, physical similarity, and spatial proximity through weighted averaging. The weights are computed using the reliability-weighted values of each parameter transfer strategy, which are also computed using the donor catchment's hydrological model's reliability value during the calibration and validation periods. The suitability of the proposed scheme was checked in the Omo Gibe and Upper Blue Nile River basins, in Ethiopia. After analyzing parameter transfer techniques using the jackknife algorithm at several target-gauging stations, it was found that the reliability-weighted method outperforms all three regionalization approaches by about 30% for the test catchments. In addition, approximately 85% of the time, the proposed strategy's regionalization performance has a metric Nash-Sutcliffe efficiency greater than 0.50. Alternatively, according to the median Efficiency of the Nash-Sutcliffe model, the newly proposed strategy reduces the spatial and temporal losses by around 0.01 and 0.02, respectively, when compared to the second-best performing option. Furthermore, the performance of the presented approach's runoff prediction at the test gauged catchments was very comparable to the performance of the on-site calibrated model. The newly proposed approach therefore proved to be a useful tool for assessing the potential of available water resources in ungauged catchments. Finally, the uncertainty in runoff prediction at ungauged basins was reduced by combining the strengths of parameters transfer strategies relying on their reliability at test gauging stations.

Classification and Identification of Non-point Source Nitrogen Pollution in Surface Flow of the Shangwu River Watershed in the Qiandao Lake Region

Accurate quantification of non-point source pollution is an important step for non-point source pollution control and management at the watershed scale. Considering the non-point source pollution from baseflow, an improved export coefficient model (IECM) on a weekly scale was established based on the traditional export coefficient model (ECM), which was then used to estimate the surface flow non-point source total nitrogen (TN) loads contributed by different land use types of the Shangwu River watershed in the Qiandao Lake Region. The results showed that IECM performed well for the predictions of TN loads in the studied watershed, with the Nash-Sutcliffe efficiency coefficient (NSE) and R2values of 0.82 and 0.77 (P<0.01) for the calibration period and 0.87 and 0.84 (P<0.01) for the validation period, respectively. The IECM estimated TN exports through surface flow and baseflow were 5.74 kg·(hm2·a)-1and 9.85 kg·(hm2·a)-1 from the Shangwu River watershed in the period of Nov. 2020 to Oct. 2021, which accounted for 36.80% and 63.20% of the corresponding streamflow TN load, respectively. Without consideration of the baseflow non-point source TN pollution, the ECM-estimated surface flow TN loading was 54.21% higher than that estimated by IECM. Obviously, attributing baseflow non-point source pollution to surface flow directly would lead to a serious load overestimation of surface flow. According to IECM, the estimated TN export intensity through surface flow from paddy fields, grasslands, woodlands, rainfed croplands, and residential lands was 10.95, 5.42, 5.20, 12.34, and 2.77 kg·(hm2·a)-1, respectively, which accounted for 5.80%, 4.00%, 26.55%, 0.38%, and 0.03% of the corresponding total streamflow TN loads. Therefore, the future management of non-point source nitrogen pollution in the studied watershed should focus mainly on the prevention and management of groundwater non-point source pollution and control of load export from surface flow on cultivated land (paddy fields and rainfed croplands).

Error correction for IMERG precipitation estimates based on climatological adjustment combining the dry–wet season division and weight allocation

Error correction to obtain more accurate gridded precipitation estimates enhances the application prospects of the integrated multi-satellite retrievals for global precipitation measurement (IMERG). This study developed a climatological adjustment approach combining dry–wet season division and weight allocation (i.e., the DW approach) to achieve IMERG estimate correction without the need for contemporaneous gauge data. By learning the characteristics of an 18-year bias series from June 1, 2000 to December 31, 2017, through empirical probability calculation (the probability distribution function of every bias value in the dry and wet seasons), the bias field can be obtained without mathematical distribution assumptions for the biases. After recognizing the bias field at each of the 687 gauges in mainland China, the error adjustment can be tailored for every gauge to lower the current bias magnitude. The bias adjustment can be obtained as the product of two components, namely, the adjustment amount (AA) and the scaling factor (SF). The SF was discussed under different value intervals of the IMERG estimates to decide whether the original IMERG estimates should subtract or add the AA to generate the adjusted IMERG estimates. The quantile mapping (QM) approach was designed to be the standard method to examine the DW approach proposed. During the validation period from January 1, 2018, to December 31, 2019, the capability of two error adjustment approaches to the value reduction of evaluation indicators was inspected at gauge, climatic, and topographic region scales. The main conclusions follow: (1) The DW approach performed better than the QM approach regarding the results of all of the evaluation indicators at the gauge scale. (2) The DW approach maintained higher efficacy than the QM approach at the gauge, climatic, and topographic region scales. (3) The proportion of the DW approach as the better-performing adjustment approach was higher than the QM approach both at the gauge scale and in most subregions. (4) In the areas where the IMERG products greatly overestimate the precipitation, the DW approach will be very effective, whereas the QM approach will be very effective in the areas where the precipitation is significantly underestimated by the IMERG data.

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

AbstractSatellite‐derived shoreline observations combined with dynamic shoreline models enable fine‐scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite‐data‐assimilated, “littoral‐cell”‐based, ensemble Kalman‐filter shoreline model to predict coastal change and uncertainty due to waves, sea‐level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water‐level‐corrected, satellite‐derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea‐level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20‐year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model‐predicted shoreline positions to satellite‐derived observations, the model achieves an accuracy of &lt;10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi‐decadal simulations of shoreline change due to projected wave and sea‐level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite‐data‐assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.

Performance assessment of methods to estimate initial hydrologic conditions for event-based rainfall-runoff modelling

Abstract Event-based hydrological models are extensively adopted for the estimation of design floods and in operational flood forecasting frameworks. However, an accurate estimation of the initial hydrologic condition (IHC) is essential in enhancing the predictive capability of an event-based hydrological model. Hence, in this study, IHCs of an event-based conceptual model are estimated using two different methods: (1) assimilation of observed variables such as streamflow and soil moisture using an ensemble Kalman filter and (2) states obtained from the continuous model calibrated using four different calibration metrics. The observed flood events at the Jagdalpur catchment are simulated using a conceptual hydrologic model setup at two spatial resolutions (lumped and semi-distributed). The results of the study demonstrate that IHCs estimated by the continuous models perform better than those obtained through data assimilation. The performance of semi-distributed event-based models was found to be outperforming their lumped counterparts demonstrating the advantage of increased model resolution. The states obtained from the continuous models calibrated using Nash–Sutcliffe Efficiency (NSE) are performing well in initialising the event-based models. The median efficiency of the semi-distributed event-based model (based on states from the NSE calibrated continuous model) is 0.91 and 0.77 during calibration and validation periods, respectively.

Стан та перспективи розвитку малого і середнього підприємництва в умовах війни в Ук

This paper investigates the current state of small and medium business in Ukraine. The protracted nature of the war in Ukraine significantly destabilizes the activities of the subjects of this sector and leads to the deterioration of its development indicators. In particular, a decrease in quantity of active entities small and medium business, a decrease in the rate of registration of new entities, a shortage of own working capital in most small and medium enterprises will have a negative impact on the national economy. The main obstacles to the recovery and development of business activity during the period of validity of state of war are identified. It is the lack of financial resources; continuation of hostilities; interruptions in electricity, water and heat supply; destruction of logistics; increase in production costs; insufficient quantity of paying customers. The peculiarities of state support of small and medium business in Ukraine during the war are considered. The problems hindering the development of national small and medium business in the conditions of war are identified. It is the absence of a program document on the development of the small and medium business sector; low level of access to finance by small and medium business entities; insufficiently developed state financial support programs for IT startups; inefficiency of industrial parks. Proved, that the prospects of the development of small and medium business largely depend on ensuring the implementation of an effective state policy to stimulate its development. In this context, the priorities of the state policy in the sphere of stimulating the development of small and medium business in the conditions of the war in Ukraine are substantiated. It is the development of the State program for promoting the development of small and medium business in Ukraine, coordination of strategies and programs for the development of small and medium business at the state, regional and local levels; attraction of money from international financial organizations and international donors; creation of conditions for increasing the level of access of small and medium enterprises to finance; expansion of state grant support for IT startups; implementation of mechanisms of tax benefits for entities that resume their activities in the de-occupied territories; expansion and creation of new industrial parks with developed engineering and transport infrastructure, creation of favorable conditions for the development of small and medium business within the industrial park.

THE ROLE OF NOTARY/PPAT (LAND DEED OFFICIAL) IN MAINTAINING THE PREFERRED POSITION OF CREDITORS WITH THE MAKING OF DEED OF GRANTING COLLATERAL AND POWER OF ATTORNEY

The Mortgage is a guarantee institution over land that is erased if the Land Rights entrusted with it are erased. One of the reasons for the abolition of land rights is when the rights expire or are in the process of renewing rights. Renewal of rights is the addition of the validity period of a right after the expiration period or before the expiration of the renewal period. The research is normative juridical legal research and has an analytical perspective, with research data in the form of secondary data collected by document study. The collected data will be analyzed using conceptual, statute, and case approaches. Based on the data obtained, it can be concluded that the role of the Notary in maintaining the preferred position of the creditor is to assist the debtor in renewing rights for the benefit of the creditor who has lost the object of collateral, as well as making a Deed of Granting Collateral and Power of Attorney which contains the debtors promise to fulfill his achievements and rebinding the collateral object after completion of the rights renewal process.

The effect of the removal of regional anti-COVID restrictive measures on the dynamics of applications for unemployment benefits in Russia

This paper assesses changes in the dynamics of applications for unemployment benefits in response to the abolition of regional restrictive measures during the first wave of COVID-19 spread in Russia. This assessment is interesting from the perspective of developing anti-crisis support measures for the population. The assessment is based on weekly-regional panel data using the staggered difference-in-differences method. After the lifting of restrictive measures, the number of new applications for unemployment benefits does not decrease significantly. The result remains robust when an alternative measure of the stringency of restrictions is used, such as an indicator for the validity period of digital passes instead of data on the stages of lifting restrictions. A comparison of official data on the effect of restrictive measures with the Yandex self-isolation index is provided.

Reconstructing Groundwater Storage Changes in the North China Plain Using a Numerical Model and GRACE Data

Groundwater has been extensively exploited in the North China Plain (NCP) since the 1970s, leading to various environmental issues. Numerous studies have utilized Gravity Recovery and Climate Experiment (GRACE) satellite data to analyze changes in groundwater storage in the NCP and provide valuable insights. However, the low spatial resolution of GRACE data has posed challenges for its widespread application, and there have been limited studies focusing on refining groundwater storage changes in the NCP. In addition, the lack of data on the gap period between GRACE and GRACE-FO hinders in-depth research on regional groundwater storage anomalies (GWSA). This paper applied a groundwater storage model called NGFLOW-GRACE to construct a groundwater storage change model in the NCP at spatial resolutions of both 1° and 0.05°. The groundwater storage change model was calibrated and driven using gratis data, with hydrogeological parameter values estimated using the shuffled complex evolution algorithm (SCE-UA). The model exhibited favorable performance, with correlation coefficients greater than 0.85 during the calibration period and 55% of coefficients greater than 0.50 during the validation period. Interestingly, the results indicate that different combinations of remote sensing data do not significantly impact the outcomes, while the hydraulic gradient coefficient demonstrates the highest sensitivity. Appropriate reconstructed data were selected within the empty window period, and by downscaling the model to a resolution of 0.05°, a complete cycle (January 2003 to December 2020) of GWSA was derived. Through comprehensive comparisons with previous research findings on both temporal and spatial scales, it can be concluded that the downscaled groundwater storage changes obtained from the established model demonstrated high reliability.

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (&amp;gt;18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables.

ENABLING KNOWLEDGE SHARING BY MANAGING DEPENDENCIES AND INTEROPERABILITY BETWEEN INTERLINKED SPATIAL KNOWLEDGE GRAPHS

Abstract. Knowledge sharing is increasingly being recognized as necessary to address societal, economic, environmental, and public health challenges. This often requires collaboration between federal, local, and tribal governments along with the private sector, nonprofit organizations, and institutions of higher education. To achieve this, there needs to be a move away from data-centric to knowledge sharing architectures, such as a Geospatial Knowledge Infrastructure (GKI). Data from multiple organizations need to be properly contextualized in both space and time to support geographically based planning, decision making, cooperation and coordination. A spatial knowledge graph (SKG) is a useful paradigm for facilitating knowledge sharing and collaboration. However, interoperability between independently developed SKGs from different organizations that reference the same geographies is often not automated in a machine-readable way due to a lack of standardization. This paper outlines an architecture that automates interoperability and dependency management between SKGs as they are formally published by version and period of validity. We are calling this approach a spatial knowledge mesh (SKM), as it is a specialization of the data mesh architecture along with the concept of a common geo-registry to facilitate knowledge sharing more easily. The initial implementation, called GeoPrism Registry, is being developed as an open-source spatial knowledge infrastructure as a platform to help countries meet their NSDI and GKI objectives. It was fist funded and deployed to support ministries of health and is more recently being utilized in GeoPlatform.gov.

Disaggregated monthly SWAT+ model versus daily SWAT+ model for estimating environmental flows in Peninsular Spain

The Water Framework Directive requires all water bodies in EU countries to ensure the ecological integrity of freshwater ecosystems in all water bodies. A “good” status in water bodies leads to their biological elements to reproduce reference conditions, so quantity and timing should be considered in describing environmental flows. However, the availability of daily natural flow data is one of the major drawbacks, especially in highly water-regulated countries, such as Spain, and hydrological models, such as the Soil and Water Assessment Tool (SWAT+) model, can be used to generate them. This work evaluated three different approaches for obtaining daily flows used to characterise the natural regime and to estimate environmental flows in the Piloña River (northern Spain) and Tagus River (centre Spain) basins: Scenario 1) daily flows simulated by the SWAT+ model calibrated on a monthly scale; Scenario 2) daily flows estimated by applying monthly-to-daily disaggregation techniques from the monthly series obtained from the SWAT+ model calibrated on a monthly scale using Water Rights Analysis Package (WRAP) software; and Scenario 3) daily flows simulated by the SWAT+ model calibrated on a daily scale. The Kling–Gupta efficiency (KGE), percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and standard deviation ratio (RSR) were used to evaluate the model’s performance. Furthermore, the indicators of hydrologic alteration in rivers (IAHRIS) and the basic flow methods were assessed and compared with those obtained directly using the real data observed at the gauging station. Our results indicated that Scenario 2 outperformed the rest of the approaches for all the statistics used. KGE and NSE varied between 0.78 and 0.92 for both basins in Scenario 2 while these values decreased to 0.50–0.60 for KGE and 0.20–0.40 for NSE in Scenarios 1 and 3. Moreover, PBIAS ranged between 3.24 and 4.47% in validation periods in Scenario 2, while it decreased to −14.64% in Scenario 3. Likewise, Scenario 2 was also observed to show better performance related to both the patterns of the natural regime and the basic flow values, which were generally underestimated by the other scenarios. The methodology proposed provides a valuable tool for evaluating and managing the hydrological and environmental status of river basins.

The sui generis right to non-original objects generated by a computer program: novelties of legal regulation

The article considers novel legal regulations regarding non-original objects generated by a computer program. It was established that at the level of the law, legal certainty was introduced regarding the legal regime of these objects: the concept and characteristics of a non-original object, generated by a computer program, subjects of property rights, the scope of such rights, their validity period, the possibility of transfer (alienation) were defined) of these rights, as well as an opportunity to properly protect such rights. The signs of a non-original object generated by a computer program are defined: difference from already existing similar objects; creation of an object solely as a result of the operation of a computer program without direct human participation. It has been proven that the independent legal personality of artificial intelligence is highly debatable, and this concept goes far beyond the boundaries of intellectual property law. It is justified that giving artificial intelligence an independent legal personality as of today is a hasty step, therefore the approach reflected in the law deserves attention.It is substantiated that the legal regime of an object that is non-original and that is generated by a computer program is very similar to the legal regime of a work as an object of copyright, which enables the relevant subjects to exercise and protect the rights to these objects. It was established that as a result of the creation of a non-original object generated by a computer program, personal non-property rights do not arise at all, and property rights belong to the owner of the property rights to the computer program that generated such an object. It has been proven that the property rights to a nonoriginal object are in fact identical to the property rights of the author (other subjects of property copyright), the scope of which is determined by Article 12 of the Copyright Law.It is established that when creating a non-original object generated by a computer program, one should observe the property rights to other non-original objects generated by a computer program, which are used in the process of generating a new non-original object; to objects of copyright and/or related rights that were used in the process of generating such non-original object.

Electric taxi licensing under a tradable credit scheme: model and application

ABSTRACT Taxi market modeling has long attracted researchers’ and practitioners’ attention, with a variety of models presented to investigate the effects of different market regulation regimes. Motivated by the increasing interest in and theoretical advantages of tradable credit systems for mobility management, this study investigates the use of a tradable credit scheme for taxi licensing. A mixed integer non-linear programming problem is formulated to determine the credit price, daily charge and number of vehicles in order to maximize social welfare. In the social benefit-maximizing solution, the supply of conventional taxis is exhausted, while about 50% of the available electric taxi drivers enter the market. Sensitivity analysis shows that increasing the validity period of the credits, reducing the daily system fee for electric taxis or subsidizing the purchase of electric taxis could lead to greater social benefits by increasing the profit margin of electric taxi drivers and incentivizing their wider adoption.

Evaluating the effects of plastic film mulching duration on soil nitrogen dynamic and comprehensive benefit for corn (Zea mays L.) field

Regulating the plastic film mulching duration was a good strategy to decrease plastic residues and to avoid the stress from low soil nitrate concentration (SNC). However, few studies focused on soil nitrogen dynamic under different mulching durations, especially for the impacts of soil nitrogen loss on crop yield and comprehensive benefits have not been revealed. In this study, HYDRUS (2D/3D) model was used to evaluate the differences of SNC and NO3-N leaching (NL) under no mulching (NM), mulching for the seedling stage (PMS), mulching for the elongation stage (PME), mulching for the tasseling stage (PMT), and mulching for the entire corn growth season (PMW). Additionally, a two-year (2019–2020) field experiment was carried out to measure SNC, NL, N2O, and NH3 emissions and to calibrate and validate the model accuracy. The effect of soil nitrogen loss on the comprehensive benefits under different mulching durations was further analyzed using a comprehensive multi-objective model. The results showed that HYDRUS (2D/3D) could well capture SNC dynamic under different mulching treatments, with average the normalized root mean square error of 16.5% in the validation period. There was no apparent difference in SNC, N2O emission, NH3 emission, and NL under different mulching durations in the seedling stage. However, the corresponding difference markedly increased with corn growth and development, especially in 2019. The average SNC (0–20 cm soil depth) under NM was higher by 9.2%, 25.0%, 21.2%, and 19.4% than PMS, PME, PMT, and PMW, respectively. Nitrogen emission (N2O and NH3) showed an increase trend with the shortening of mulching duration, i.e., NM>PMS>PME >PMT >PMW, while an opposite trend was found for NL. In general, PME was found to be the optimal mulching duration with the highest comprehensive evaluation score of 1782.7. Therefore, PME can be recommended to apply in this region to promote the developments of sustainable agriculture.

Exotic option pricing model of the Black–Scholes formula: a proactive investment strategy

The option is an important derivative tool in financial market, and after decades of development, the option has emerged in various forms. This paper studies an exotic option with a proactive investment strategy. Compared with the classic option theory, we assume that the option holders continuously trade the underlying assets according to a predetermined investment strategy. Taking the European call option as an example, we first give some assumptions and define the loss function in terms of a logarithmic investment strategy. Then, the specific pricing expression of the exotic option is derived from the Black-Scholes option pricing formula. Next, numerical simulations are presented to visualize the mechanism of the exotic option in 2D and 3D forms by selecting appropriate parameters. The results indicate that the exotic option has a significant price advantage (up to 43.9% under specific parameter settings) over the classic option. Moreover, the empirical results illustrate a perfect fit between 50 ETF (issued by the Shanghai Stock Exchange) and our exotic option. The new proposed exotic option extends the Black-Scholes option theory from a no-trading condition (do not buy or sell underlying assets during the validity period) to a dynamic investment condition and has important practical significance in real life.

Runoff Simulation under the Effects of the Modified Soil Water Assessment Tool (SWAT) Model in the Jiyun River Basin

Few studies have been conducted to simulate watersheds with insufficient meteorological and hydrological information. The Jiyun River watershed was selected as the study area. A suitable catchment area threshold was determined by combining the river network density method with the Soil and Water Assessment Tool (SWAT) models, which was driven using the CMADS dataset (China Meteorological Assimilation Driving Datasets for the SWAT model). Monthly runoff simulations were conducted for the basin from 2010 to 2014, and the calibration and validation of model parameters were completed with observed data. The results showed that the final expression for the density of the river network in the Jiyun River basin as a function of density (y) and the catchment area threshold (x) was obtained as y = 926.782x−0.47717. The “inflection point” of the exponential function was the optimal catchment area threshold. The catchment area threshold had an upper and lower limit of the applicable range and was related to the percentage of the total basin area. The simulation results would be affected if the threshold values were outside the suitable scope. When the catchment area was 1.42% of the entire watershed area, increasing the threshold value had less effect on the runoff simulation results; decreasing the threshold value would cause the simulation results to be unstable. When the catchment area reached 1.42% to 2.33% of the total watershed area, the simulation results were in good agreement with the observed values; the coefficient of determination (R2) and Nash–Sutcliffe efficiency coefficient (NSE) were more significant than 0.79 and 0.78 for the calibration periods evaluation index. Both were greater than 0.77 and 0.76 for the validation period, which met the evaluation requirements of the model. The results showed that the CMADS-driven SWAT model applied to the runoff simulation and the river network density method adoption to determine the catchment area threshold provided a theoretical basis for a reasonable sub-basin division in the Jiyun River basin.

Effects of Climate Change on Surface Runoff and Soil Moisture in the Source Region of the Yellow River

The impact of climate change on surface runoff and soil moisture in the source region of the Yellow River is analyzed, which will provide a scientific basis for the rational use and protection of water resources in the source area. In this paper, the SWAT hydrological model was coupled with the Coupled Model Intercomparison Project (CMIP) to predict future changes in surface runoff and soil moisture in the source region of the Yellow River. The prediction of surface runoff and soil moisture in the Yellow River Basin was analyzed by a linear regression model. The SWAT model rate had a calibration period R2 of 0.876 and a validation period R2 of 0.972. The trend of surface runoff and annual mean temperature in the source region of the Yellow River from 2011 to 2022 showed an overall increasing trend, and soil moisture showed a general decreasing trend. 2011–2022 trends between surface runoff and annual mean temperature in the source region of the Yellow River showed a highly significant difference, indicating that surface runoff flow was significantly influenced by temperature. The difference between the trends in soil moisture and the annual mean temperature was highly significant. The surface runoff fluctuated greatly in different years, and the surface runoff changed greatly in different scenarios of CMIP5 (RCP2.6, RCP4.5, and RCP8.5). For all three climate change scenarios, the surface runoff displayed a downward trend. The surface runoff showed a similar uneven distribution for all scenarios on a yearly cycle. Under the three climate scenarios, the runoff was highest between May and August, with a slowly increasing trend from January to April and a slightly decreasing trend from September to December. The interannual and interannual distribution of soil water was basically consistent with the distribution of surface runoff, and there was an overall trend in the length of all soil water reduction scenarios. Surface runoff and soil moisture are and will be greatly affected by climate change (mainly temperature and precipitation). Under the three climate scenarios, the precipitation increases to some extent, but the surface runoff and soil moisture will both decrease, which may be attributed to the greater evaporation than the precipitation.

Plant–water relations in subtropical maize fields under mulching and organic fertilization

The plant–water relationship of maize under conservation practices needs to be assessed to quantify the effectiveness of the practices in conserving soil water for crop production. This study evaluated in three trials how straw and plastic film mulching and organic manure application could potentially change water fluxes in the root zone and increase maize yield. A mathematical model HYDRUS-1D was calibrated against the observed soil water content and drainage data to predict the water fluxes in the root zone soil. The model simulated soil water dynamics in the root zone with satisfactory performance (RMSE of 0.6–2.3%, CD of 0.37–1.41, NSE of 0.18–0.88, and R2 of 0.62–0.91) during both the calibration and validation periods. The model predicted the observed drainage in a lysimeter with only a 5.5–11.7% bias and actual evapotranspiration (ETc) with a 2.6–6.7% bias for the control conditions in all three trials when the model was provided with measured plant growth, soil properties, and weather data. Both measurement and modeling confirmed that mulching augmented soil water storage by reducing ETc, i.e., 0.24–0.37 mm d-1 by straw mulching and 0.05–0.24 mm d-1 by plastic mulching during the trials. Manure application did not affect the ETc rate and resulted in the highest grain yield (6.8–8.3 Mg ha˗1) followed by plastic mulching (6.1–8.1 Mg ha˗1) and straw mulching (5.3–7.5 Mg ha˗1). Manure application increased the harvest index by optimally allocating biomass because of a steady supply of water and nutrients. The straw mulch, plastic mulch, and manure treatments increased grain yield by 13%, 24%, and 35%, respectively, compared to the control condition. Large-scale implementation of these practices would lessen blue water scarcity in agriculture.