Validation Period Research Articles

Groundwater Level Trend Analysis and Prediction in the Upper Crocodile Sub-Basin, South Africa

Disasters related to climate change regarding water resources are on the rise in terms of scale and severity. Therefore, predicting groundwater levels (GWLs) is a crucial means to aid adaptive capacity towards disasters related to climate change in water resources. In this study, a Gradient Boosting (GB) regression modelling approach for GWL prediction as a function of rainfall and antecedent GWL is used. A correlation analysis carried out from 2011 to 2020 demonstrated that monthly GWLs can be predicted by antecedent GWLs and rainfall. The study also sought to understand the long-term effects of climate events on groundwater levels over the study area through a Mann–Kendall (MK) trend analysis. A total of 50% of the groundwater stations revealed declining trends, while 25% had no trends and the other 25% showed an increasing trend. Again, the correlation analysis results were used in justifying the trends. The GB predictive model performed satisfactorily for all groundwater stations, with the MSE values ranging from 0.03 to 0.304 and the MAE varying from 0.12 to 0.496 in the validation period. The R2 ranged from 0.795 to 0.902 for the overall period. Therefore, based on projected rainfall and antecedent groundwater levels, future GWLs can be predicted using the GB model derived in this study.

The application of CHIRPS-based Pitman modelling in South Africa

Study regionThe Berg Water Management Area (WMA), drainage region G of South Africa is critical to supplying water for the City of Cape Town and the entire Western Cape region. Study focusDeveloping an approach for the application of CHIRPS-based Pitman modelling in South Africa. CHIRPS-based satellite rainfall data (with a spatial resolution of 0.05° × 0.05°) was used in the Pitman model to simulate observed flows. The model was set up for the Berg WMA which has 35 quaternary catchments and covers an area of approximately 12,965 km2 in assessing the suitability of CHIRPS-based flows. New hydrological insights for the regionThe simulated CHIRPS-based flows using the developed approach indicated 80% suitability of CHIRPS data as an alternative to the dwindling observed rainfall in simulating flows. CHIRPS rainfall data illustrated a strong correlation to observed catchment rainfall and calibration factors were developed for adjustments. Calibration (1981–2009) and validation (2010–2019) statistics indicated a good similarity between observed and CHIRPS-based flows for the period of analysis. The exceedance probability curves for the entire simulation period illustrated similar flow characteristics for the calibration and validation periods. In assessing for uncertainty, hydrological indices were used at a defined ±15% level and the results showed CHIRPS to be suitable. This research recommends the use of CHIRPS-based rainfall data as a suitable alternative in simulating flows for South Africa.

Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are widely employed as valuable tools in watershed management. For this specific study, two modelling approaches were employed: the Soil and Water Assessment Tool (SWAT) model and a set of eight artificial intelligence (AI) models. The AI models consisted of seven data-driven approaches, namely k-nearest neighbour regression, support vector regression, linear regression, artificial neural networks, random forest regression, XGBoost, and Histogram-based Gradient Boost regression. Additionally, a deep learning model known as Long Short-Term Memory (LSTM) was also utilized. The study focused on monthly streamflow modelling in the Murredu River basin, with a calibration period from 1999 to 2003 and a validation period from 2004 to 2005, spanning a total of 7years from 1999 to 2005. The results indicated that all nine models were generally suitable for simulating the rainfall-runoff process, with the LSTM model demonstrating exceptional performance in both the calibration (R2 is 0.97 and NSE is 0.96) and validation (R2 is 0.97 and NSE is 0.92) periods. Its high coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) values indicated its superior ability to accurately model the rainfall-runoff relationship. While the other models also produced satisfactory results, the findings suggest that selecting the most efficient model, such as the LSTM model, could significantly contribute to the effective management and planning of sustainable water resources in the Murredu watershed.

Evaluation of Reservoir Inflows Using Semi-Distributed Hydrological Modeling Techniques: Application to the Esil and Moildy Rivers’ Catchments in Kazakhstan

This study explores the potential application of the HBV model for simulating water inflow into the Astana reservoir in Kazakhstan. The Astana reservoir, with an area of 60.9 km2 and a capacity of 411 million m3, serves as a vital hydrotechnical structure for the regulation of long-term water resources. Positioned on the Esil River, this reservoir plays a crucial role in providing drinking water to Astana, the capital city of the Republic of Kazakhstan. This study presents a concise overview of the HBV model and includes hydrometeorological data as well as information from a digital elevation model constructed from altitude maps of the catchment areas of the Esil and Moildy rivers. To simulate the runoff from both rivers, the GAP optimization algorithm was employed. Model parameters were calibrated for the period spanning 1975 to 2020. The effectiveness of the model was evaluated using the Nash–Sutcliffe criterion, which confirms a strong correlation between the simulated runoff dynamics and observed data. This study’s outcomes reveal the efficiency of the selected optimal parameters, particularly for the 2020–2022 timeframe, during which the HBV model’s parameters were validated. Overall, the quality metrics achieved during both the calibration and validation periods are satisfactory, affirming the model’s suitability for short-term runoff forecasts, especially during flood events. Furthermore, the model proves valuable for assessing potential shifts in river runoff patterns under the influence of climate change.

A comparison of the performance of SWAT and artificial intelligence models for monthly rainfall–runoff analysis in the Peddavagu River Basin, India

Abstract Rainfall–runoff (R–R) analysis is essential for sustainable water resource management. In the present study focusing on the Peddavagu River Basin, various modelling approaches were explored, including the widely used Soil and Water Assessment Tool (SWAT) model, as well as seven artificial intelligence (AI) models. The AI models consisted of seven data-driven models, namely support vector regression, artificial neural network, multiple linear regression, Extreme Gradient Boosting (XGBoost) regression, k-nearest neighbour regression, and random forest regression, along with one deep learning model called long short-term memory (LSTM). To evaluate the performance of these models, a calibration period from 1990 to 2005 and a validation period from 2006 to 2010 were considered. The evaluation metrics used were R2 (coefficient of determination) and NSE (Nash–Sutcliffe Efficiency). The study's findings revealed that all eight models yielded generally acceptable results for modelling the R–R process in the Peddavagu River Basin. Specifically, the LSTM demonstrated very good performance in simulating R–R during both the calibration period (R2 is 0.88 and NSE is 0.88) and the validation period (R2 is 0.88 and NSE is 0.85). In conclusion, the study highlighted the growing trend of adopting AI techniques, particularly the LSTM model, for R–R analysis.

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model

The accurate prediction of monthly runoff in the lower reaches of the Yellow River is crucial for the rational utilization of regional water resources, optimal allocation, and flood prevention. This study proposes a VMD-SSA-BiLSTM coupled model for monthly runoff volume prediction, which combines the advantages of Variational Modal Decomposition (VMD) for signal decomposition and preprocessing, Sparrow Search Algorithm (SSA) for BiLSTM model parameter optimization, and Bi-directional Long and Short-Term Memory Neural Network (BiLSTM) for exploiting the bi-directional linkage and advanced characteristics of the runoff process. The proposed model was applied to predict monthly runoff at GaoCun hydrological station in the lower Yellow River. The results demonstrate that the VMD-SSA-BiLSTM model outperforms both the BiLSTM model and the VMD-BiLSTM model in terms of prediction accuracy during both the training and validation periods. The Root-mean-square deviation of VMD-SSA-BiLSTM model is 30.6601, which is 242.5124 and 39.9835 lower compared to the BiLSTM model and the VMD-BiLSTM model respectively; the mean absolute percentage error is 5.6832%, which is 35.5937% and 6.3856% lower compared to the other two models, respectively; the mean absolute error was 19.8992, which decreased by 136.7288 and 25.7274 respectively; the square of the correlation coefficient (R2) is 0.93775, which increases by 0.53059 and 0.14739 respectively; the Nash–Sutcliffe efficiency coefficient was 0.9886, which increased by 0.4994 and 0.1122 respectively. In conclusion, the proposed VMD-SSA-BiLSTM model, utilizing the sparrow search algorithm and bidirectional long and short-term memory neural network, enhances the smoothness of the monthly runoff series and improves the accuracy of point predictions. This model holds promise for the effective prediction of monthly runoff in the lower Yellow River.

Mixed Use of Chemical Pesticides and Biopesticides among Rice–Crayfish Integrated System Farmers in China: A Multivariate Probit Approach

Biopesticides are perceived as a feasible alternative to chemical pesticides, providing an effective approach to pest management while mitigating the undesirable effects of chemical pesticide overuse. Yet, due to the distinctive attributes of the two types of pesticides, farmers often adopt a combination of both. This study aimed to probe the interdependent relationship underlying the mixed use of chemical pesticides and biopesticides by farmers in the rice–crayfish integrated system (RCIS) in Hubei province, China. We also sought to identify influencing factors and associated consequences of this practice. Given that the adoption of chemical pesticides and biopesticides by farmers is not mutually exclusive, a multivariable probit model was utilized to estimate simultaneous pesticide applications. Survey data from 736 RCIS farmers revealed that 10.50% of the sample farmers employ a combination of chemical pesticides and biopesticides. A substitution relationship was observed between the adoption of chemical pesticides and biopesticides by farmers, whereas no significant complementary relationship was found in the application of the two types of pesticides. The primary motivation for the mixed use of pesticides by farmers is to achieve superior pest control efficacy. Notably, when integrating the use of pesticides, farmers pay less attention to factors such as resource endowments and multiple production objectives. Further analysis indicated that farmers display significant concern for pesticide attributes including insecticidal efficacy, toxicity, insecticidal spectrum, and validity period. The mixed use of pesticides has resulted in a decreased frequency of pesticide application but has escalated the cost. While the mixed use of pesticides elevated the selling price of rice, no significant improvement was observed in yield and output value. The insights garnered from this study offer strategic implications for policymakers on how to enhance the judicious adoption of pesticides by farmers.

Runoff Simulation and Climate Change Analysis in Hulan River Basin Based on SWAT Model

The shortage of water resources is a long-standing constraint on the development of the Chinese economy and society. In this paper, the climate change occurring in Hulan River Basin is analyzed using the data collected at Wangkui Meteorological Station from 1960 to 2020. The overall temperature in the basin shows an upward trend, with a cumulative increase of 1.6 °C, as does the precipitation, which reaches 566.2 mm. In contrast, there is a downward trend shown by wind speed, with a cumulative decrease of 1.313 m/s. GIS remote sensing technology is applied to build a SWAT distributed hydrological model for the purpose of conducting runoff simulation in Hulan River Basin, and SWAT-CUP software is used to correct and analyze the simulation results. The parameters of snow melt are set to improve the accuracy of the model. The runoff data collected from Lanxi Hydrological Station from 2008 to 2020 are used to verify the model. The results show that the efficiency coefficient (NES) and correlation coefficient (R2) are 0.75 and 0.84, respectively, in the validation period from 2010 to 2013, while they are 0.77 and 0.93, respectively, in the correction period from 2014 to 2016, meeting the criteria of model evaluation. It can be seen from results noted above that SWAT is applicable in Hulan River Basin, providing a certain reference for the management of hydrological and water resources available in this region and for the construction of a distributed hydrological model of rivers in those high-latitude cold regions.

Evaluating soil water dynamics under a no-tillage system with residual plastic film mulching in an arid region of western China using HYDRUS-2D

No-tillage (NT) with residual plastic film mulching from an earlier year (NTR) is considered an excellent agricultural strategy in arid regions, recommended to reduce not only soil disturbance but also plastic film residues. However, the soil water dynamic in the root zone under this management is more complex due to variations in soil properties and upper boundary conditions. Soil compaction and a disintegrated fraction (DF) of the residual plastic film increase in NTR years. Soil water movement under NTR is not fully understood. It is also necessary to determine a suitable number of NTR years and optimize irrigation to increase water use efficiency (WUE) and maintain sustainable agricultural development. Therefore, the HYDRUS-2D model was calibrated and validated using experimental data from 2019 to 2021 collected in the corn field with conventional tillage (CTN), NT with a new plastic film (NTN), and NTR. The soil water contents (SWC), cumulative water fluxes (CWF), and soil water balances under CTNH, NTNH, and NTRH with the high (subscript H) irrigation depth of 315 mm (the local recommendation) were evaluated using both measured and simulated data. The results for NTRH, NTRM (a medium irrigation depth of 252 mm, 80% of local recommendation), and NTRL (a low irrigation depth of 189 mm, 60% of local recommendation) scenarios were compared, and a suitable number of NTR years was determined. The results of numerical simulations agreed with observations, with the mean relative error range for the calibration and validation periods between 4.9% and 5.4%. The larger number of experimental NTR years, the larger the SWC differences for all irrigation depths, tillage practices, and mulching due to larger soil compaction under NT and a higher DF of the residual plastic film. The largest SWC differences occurred in the 0–30 cm soil layer in the third experimental year (2021), with an average SWC decrease of 2.3% for NTNH compared to CTNH and 10.2% for NTRH compared to NTNH. Simulated two-dimensional SWC distributions further confirmed these experimental results. There were lower CWF under NT and new film mulching. CWF at a depth of 30 cm was 8.5 mm lower for NTNH than for CTNH in 2021. There was a 4.2%, 30.1%, and 43.1% increase in CWF for NTRH compared to NTNH due to higher soil evaporation evaluated from the soil water balance in 2019, 2020, and 2021, respectively. The scenario simulation results showed that SWCs decreased linearly with an increase in DF and that DF of 30% can be regarded as the maximum value for obtaining high WUE under NTR. The highest WUE of 28.3 kg ha–1 mm–1 under NTR during two years was found when the local recommended irrigation depth was reduced by 20% (to 252 mm). Therefore, this management can be recommended in arid regions to avoid plastic pollution with higher WUE.

Application of SWAT and WEAP Models for Sustainable Management of Water Resources in the Two Rivers Dam Catchment, Uasin Gishu County, Kenya

Kaptagat Forest, the main source of the Ellegerini River, which feeds the Ellegerini Dam and Two Rivers Dam, is under threat of extinction due to human activity. The Two Rivers Dam catchment that has over the years been a source of water in Uasin Gishu County is slowly depleting and urgent measures are required to restore it. Activities including commercial logging, charcoal burning and firewood harvesting have exerted a lot of pressure on the catchment, posing a great threat to the livelihoods of the people of Eldoret town who depend on the reservoirs for water supply. The main objective of this study was to customise SWAT and WEAP models for the sustainable management of water resources in the Two Rivers Dam catchment. The specific objectives were to set up and apply the SWAT model to generate simulated river flows draining to the Two Rivers and Ellegerini Reservoirs as an input to the WEAP model to determine the impact of land use change on the hydrological function of the Two Rivers Dam catchment, to set up, calibrate and validate a WEAP model for the Two Rivers Dam catchment and to apply the WEAP model in analyses of various management and infrastructural development projects scenarios to enhance river flow and water storage in the Two Rivers and Ellegerini Reservoirs. The goodness of fit SWAT model statistical evaluation indices attained during the calibration period was R2 = 0.854, NSE = 0.822 and Bias = 0.392. Additionally, for the validation period, the R2 = 0.786, NSE = 0.815 and Bias = 0.381. The modelled results indicate that the land use change resulted in decreased baseflow and increased surface runoff hence the high fluctuations of water levels in the Two Rivers and Ellegerini reservoirs. The WEAP model results for actual and simulated water demand in the calibration period of 2019, the R2 = 0.88, while during the validation period in the year 2020, the R2 = 0.85. The results of the model simulation indicated that the management option that had the most impact on all the scenarios was the reduction of unaccounted-for water, while the one with the least impact was increased water use efficiency. It was concluded that the models were able to simulate the observed conditions reasonably well and can therefore be used to effectively manage water resources and assist the relevant stakeholders in decision-making. The study recommended that forested areas need to be properly conserved in order to restore the hydrological function of the catchment.

Evaluation of flood disaster management planning: a comparative method for Bostwana and Indonesia

Disaster management planning is an activity that is translated into documents to be used as a reference in carrying out disaster risk reduction.. Furthermore, based on data from UN-ESCAP for 2022, disaster events in Indonesia dan Bostwana are dominated by extreme weather, especially storms, floods and droughts. so that disaster management planning, especially for flood disasters, is considered important as one of the efforts to reduce disaster risk. This study aims to compare the evaluation results of the disaster management planning documents owned by Botswana and Indonesia. This study uses a systematic literature review method and a comparative study with a qualitative descriptive approach to evaluation of disaster management planning between Gaborone, Botswana and Yogyakarta, Indonesia. The results of the research show that the evaluation of disaster management planning in Gaborone City, Botswana still requires updating and involving the public more in the preparation of this document. In addition, the results of the evaluation of disaster management planning documents in the City of Yogyakarta, Indonesia still require efforts to integrate disaster documents with other planning documents so that they can become a reference for the implementation of disaster risk reduction activities by various stakeholders involved in the disaster sector. The problem with disaster management planning documents in the two countries is the lack of public involvement and the lack of integration of this disaster management planning document with other development planning documents, including issues regarding validity periods. The offered solution is to update the disaster management planning document by involving more public argument.

Simulation of Retrospective Morphological Channel Adjustments Using High-Resolution Differential Digital Elevation Models versus Predicted Sediment Delivery and Stream Power Variations

This work proposes a methodological approach applied to ephemeral gravel-bed streams to verify the change in the magnitude and frequency of hydrological events affecting the morphological dynamics and sediment budget in this type of channel. For the case study, the Azohía Rambla, located in southeastern Spain, was chosen, emphasizing the research on two reference riverbed sections (RCRs): an upper one, with a predominance of erosion, and a middle one, where processes of incision, transport, and deposition were involved. First, this approach focuses on relationships between peak discharges and sediment budgets during the period 2018–2022. For this purpose, water level measurements from pressure sensors, a One-Dimensional Hydrodynamic model, and findings from comparative analyses of high-resolution differential digital elevation models (HRDEM of Difference-HRDoD) based on SfM-MVS and LiDAR datasets were used. In a second phase, the GeoWEPP model was applied to the period 1996–2022 in order to simulate runoff and sediment yield at the event scale for the watersheds draining into both RCRs. During the calibration phase, a sensitivity analysis was carried out to detect the most influential parameters in the model and confirm its capacity to simulate peak flow and sediment delivery in the area described above. Values of NS (Nash–Sutcliffe efficiency) and PBIAS (percent bias) equal to 0.86 and 7.81%, respectively, were found in the calibration period, while these indices were 0.81 and −4.1% in the validation period. Finally, different event class patterns (ECPs) were established for the monitoring period (2018–2022), according to flow stage and morphological channel adjustments (overtopping, bankfull and sub-bankfull, and half-sub-bankfull), and then retrospectively extrapolated to stages of the prior simulated period (1996–2018) from their typical sequences (PECPs). The results revealed a significant increase in the number of events and PECPs leading to lower bed incision rates and higher vertical accretion, which denotes a progressive increase in bed armoring and bank erosion processes.

Evaluation of Agricultural BMPs’ Impact on Water Quality and Crop Production Using SWAT+ Model

Subsurface (or tile) drainage improves land productivity by enhancing soil aeration and preventing water-logged conditions. However, the continuous expansion of drained agricultural lands and reliance on synthetic fertilizer in the Midwestern United States have increasingly facilitated nitrate transport from agricultural fields to surface water bodies. Hence, there is a need to implement various agricultural best management practices (BMPs) in order to reduce the adverse water quality impacts resulting from excess nitrate, such as eutrophication and the formation of hypoxic zones. In this study, we used a SWAT+ model to assess the overall impacts on the riverine nitrate load and crop yield in the corn–soybean cropping system based on a combination of different management practices. The corn and soybean yields simulated with the model were found to be in good agreement with the observed yields for both the calibration and validation periods. The long-term simulation over a period of 30 years showed a reduction in the nitrate load of up to 32% without impacting the crop yield. The model results suggest that by reducing the current N application rate by 20% and using a 40:60 split between spring pre-plant and side-dressing N applications combined with cereal rye as a cover crop in corn–soybean rotation, one can potentially reduce nitrate losses without impacting crop yields. This study will help researchers, stakeholders, and farmers to explore and adopt alternative management practices beneficial for offsetting the environmental impacts of agricultural productions on the watershed scale.

TreeLSTM: A spatiotemporal machine learning model for rainfall-runoff estimation

Study regionThe Jinsha River (JRB) and Han River basins (HRB), China. Study focusDue to challenges in interpreting neural network algorithms in hydrological runoff processes, our research focuses on enhancing the temporal dependency and spatial correlation learning capacities. To address these issues, we proposed a multi-layer spatiotemporal model, i.e., the tree long short-term memory model (treeLSTM), for runoff estimations. Because downstream hydrological stations are affected by their own historical and current upstream rainfall and runoff, the treeLSTM’s horizontal and vertical inputs are historical and upstream rainfall-runoff for temporal and spatial features extraction, respectively. The daily rainfall-runoff from 1992 to 2016 in JRB and that from 2011 to 2020 in HRB are predicted. New hydrological insights for the regionResults of validation periods (2012–2016 in JRB, 2019–2020 in HRB) indicate that the treeLSTM in terms of (JRB: RMSE (root mean square error) = 0.5375, MAPE (mean absolute percent error) = 8.27 %, NSE (Nash–Sutcliffe model efficiency coefficient) = 0.9621; HRB: RMSE = 3.3562, MAPE = 2.91 %, NSE = 0.9934) outperformed the backpropagation neural network (BP) and LSTM models. Therefore, the treeLSTM has considerable advantages in runoff estimation due to its integration of time dependence in historical hydrological data with the spatial correlation of meteorological and hydrological elements, which may be useful for enhancing the physical interpretability of machine learning algorithms.

Dynamic prediction modeling of postoperative mortality among patients undergoing surgical aortic valve replacement in a statewide cohort over a 12-year period

ObjectiveClinical prediction models for surgical aortic valve replacement mortality, are valuable decision tools but are often limited in their ability to account for changes in medical practice, patient selection, and the risk of outcomes over time. Recent research has identified methods to update models as new data accrue, but their effect on model performance has not been rigorously tested. MethodsThe study population included 44,546 adults who underwent an isolated surgical aortic valve replacement from January 1, 1999, to December 31, 2018, statewide in Pennsylvania. After chronologically splitting the data into training and validation sets, we compared calibration, discrimination, and accuracy measures amongst a nonupdating model to 2 methods of model updating: calibration regression and the novel dynamic logistic state space model. ResultsThe risk of mortality decreased significantly during the validation period (P < .01) and the nonupdating model demonstrated poor calibration and reduced accuracy over time. Both updating models maintained better calibration (Hosmer-Lemeshow χ2 statistic) than the nonupdating model: nonupdating (156.5), calibration regression (4.9), and dynamic logistic state space model (8.0). Overall accuracy (Brier score) was consistently better across both updating models: dynamic logistic state space model (0.0252), calibration regression (0.0253), and nonupdating (0.0256). Discrimination improved with the dynamic logistic state space model (area under the curve, 0.696) compared with the nonupdating model (area under the curve, 0.685) and calibration regression method (area under the curve, 0.687). ConclusionsDynamic model updating can improve model accuracy, discrimination, and calibration. The decision as to which method to use may depend on which measure is most important in each clinical context. Because competing therapies have emerged for valve replacement models, updating may guide clinical decision making.

Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM

Accurate prediction of runoff is an important foundation for optimizing water resource allocation and reservoir scheduling operations. However, due to its complex characteristics such as time-varying and non-stationary, accurate prediction of the runoff process is very difficult. This paper proposes a novel approach for runoff forecasting by combining the physical mechanism models with the Long Short-Term Memory network (LSTM) method. Utilizing the simulation and description capabilities of physically based models, as well as the powerful nonlinear analysis provided by big data methods, a type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. Additionally, to accommodate the comprehensive analysis and evaluation of multi-model forecasting performance, this paper also proposed a comprehensive evaluation metric for runoff forecasting considering the characteristics of group models. The results of the case study demonstrate that this strategy can obtain model combinations suitable for different watershed characteristics and effectively improve the forecast accuracy of multiple models. Model combinations sorted by validation period RMSE and R2 should be a superior choice. When evaluating the runoff forecasting accuracy of obtained optimal model combination during the calibration period, the average reduction of Root Mean Square Error (RMSE) is 39.62%, and the average increase of Nash-Sutcliffe coefficient R2 is 7.49%. During the validation period, the average reduction of RMSE is 62.68% and the average increase of R2 is 24.24%. When the model combinations sorted by validation period F score, the obtained model combination increased RMSE by 22.7% and decreased R2 by 7.3% comparing to the model combination selected by RMSE and R2. It is indicated that the F score may not be suitable for evaluating model selection. The method proposed in this article can effectively improve the overall forecasting performance of a single forecasting model and has good practical value for promotion and application.

Development and Evaluation of Multi-week Tropical Cyclone Strike Probability Forecasts in the Philippines

Tropical cyclone (TC) forecast, provided a few weeks in advance, can be beneficial for the preparation and mitigation of disaster risks in TC-vulnerable countries such as the Philippines. In this study, TC strike probability forecasts with a lead time of up to 4 wk were derived by combining the TC tracks from each ensemble member of the three models – namely, NCEP Coupled Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) – to show how likely it is for a TC to form or strike an area within the 300-km radius of the TC center. To assess its performance, real-time-derived TC strike probability forecasts over the Tropical Cyclone Information Domain (TCID) of the Philippines (bounded by 0–27 °N and 110–155 °E) covering the period from 06 Oct 2020–31 Oct 2022 were evaluated. Verification metrics revealed that the skill and reliability of the TC forecasts vary with lead time and the TC being forecasted. Week 1 forecasts are reliable and can be helpful for decision-making, whereas Week 2 forecasts are considered most reliable only up to the 51–60% probability interval. On the other hand, forecasts with 3–4-wk lead times are reliable for probabilities less than 20%. The case study performed using five TCs with different intensity classifications has shown that generally, forecasts for TCs with stronger intensity have higher skill than forecasts for relatively weaker ones. It was also observed that the magnitude of the probability values varies with the intensity changes within the validity period. These findings suggest that multi-model ensemble forecasts can be utilized for the improvement and eventual operationalization of multi-week TC strike probability forecasts over the Philippines.

Developing an integrated PCE-ANOVA-RF method for uncertainty quantification of hydrological model – The Amu Darya River Basin in Central Asia

Uncertainty quantification of hydrological model parameters plays an important role in the streamflow prediction of river basins. It is still a challenge to evaluate the effect of parameter uncertainty on hydrological models in association with moderate computation and high accuracy. In this study, a PCE-ANOVA-RF (abbreviated as PAR) method is developed to analyze the individual and interactive effects of multiple uncertain parameters in the Soil and Water Assessment Tool (SWAT) model as well as generate probabilistic forecasts of daily streamflow. The proposed PAR method can not only unveil the effect of parameter uncertainty and save computation time without reducing the simulation accuracy, but also expand PCE’s ability to predict future streamflow processes. The Amu Darya River Basin in Central Asia is introduced to verify the feasibility and applicability of PAR. The results disclose that (i) compared with the Sequence Uncertainty Fitting ver-2 (SUFI-2) algorithm, the simulation accuracy of PAR is acceptable (e.g., NSE = 0.85 in calibration period and 0.81 in validation period); (ii) in terms of variance analysis and streamflow simulation, PAR is more efficient and can largely reduce the number of model runs (about 88%); (iii) the parameters related to snow melting process have significant effects on both average flow and peak flow; besides, the interaction among these parameters are more obvious than that of other sensitive parameters.

Some Current Trends in Tax and Legal Regulation

The article is devoted to the analysis of some modern trends emerging in the Russian tax and legal regulation. The author notes that in addition to the traditional legislative regulation of tax relations, designed for an unlimited period of validity, in recent years, regulatory legal acts of a temporary nature have become important. Norms with a limited validity period may be contained either in acts of the Government of the Russian Federation and the supreme executive bodies of state power of the subjects of the Russian Federation exercising the temporary powers granted to them by tax legislation to adopt them, or in separate federal laws providing for the application of these norms as an experiment. Special attention is paid to the letters of the Federal Tax Service of Russia establishing temporary bans on the implementation by territorial tax authorities of certain powers enshrined in the Tax Code of the Russian Federation. This, in fact, indicates that such letters limit the validity of tax legislation for a certain period.

The impact of multiple non-pharmaceutical interventions for China-bound travel on domestic COVID-19 outbreaks.

Non-pharmaceutical interventions (NPIs) implemented on China-bound travel have successfully mitigated cross-regional transmission of COVID-19 but made the country face ripple effects. Thus, adjusting these interventions to reduce interruptions to individuals' daily life while minimizing transmission risk was urgent. An improved Susceptible-Infected-Recovered (SIR) model was built to evaluate the Delta variant's epidemiological characteristics and the impact of NPIs. To explore the risk associated with inbound travelers and the occurrence of domestic traceable outbreaks, we developed an association parameter that combined inbound traveler counts with a time-varying initial value. In addition, multiple time-varying functions were used to model changes in the implementation of NPIs. Related parameters of functions were run by the MCSS method with 1,000 iterations to derive the probability distribution. Initial values, estimated parameters, and corresponding 95% CI were obtained. Reported existing symptomatic, suspected, and asymptomatic case counts were used as the training datasets. Reported cumulative recovered individual data were used to verify the reliability of relevant parameters. Lastly, we used the value of the ratio (Bias2/Variance) to verify the stability of the mathematical model, and the effects of the NPIs on the infected cases to analyze the sensitivity of input parameters. The quantitative findings indicated that this improved model was highly compatible with publicly reported data collected from July 21 to August 30, 2021. The number of inbound travelers was associated with the occurrence of domestic outbreaks. A proportional relationship between the Delta variant incubation period and PCR test validity period was found. The model also predicted that restoration of pre-pandemic travel schedules while adhering to NPIs requirements would cause shortages in health resources. The maximum demand for hospital beds would reach 25,000/day, the volume of PCR tests would be 8,000/day, and the number of isolation rooms would reach 800,000/day within 30 days. With the pandemic approaching the end, reexamining it carefully helps better address future outbreaks. This predictive model has provided scientific evidence for NPIs' effectiveness and quantifiable evidence of health resource allocation. It could guide the design of future epidemic prevention and control policies, and provide strategic recommendations on scarce health resource allocation.