Reservoir sediment deposition: behaviour analysis and distribution predictive modelling

In item response theory (IRT), the normal and nonparametric methods have been used to estimate the ability distribution of an examinee population. The purpose of this study is to examine the characteristics of the formulas for the standard errors of the estimators of IRT ability distribution parameters (mean and standard deviation) through simulations. The simulation factors considered were test length (25 and 50), population distribution type (normal, positively-skewed and negatively-skewed distributions), sample size (500 and 2000), and number and range of ability points to be used for estimation (5 points and [-2, 2], 10 points and [-3, 3], and 20 points and [-4, 4]). Main results were as follows. First, when the population distributions were normal, the normal method tended to estimate not only the distribution parameters but also the standard errors of the parameter estimates more accurately as the number of ability points increased. Second, when the population distributions were positively or negatively skewed, the nonparametric method estimated the distribution parameters more accurately as the number of ability points increased, but the standard errors of the parameter estimates were severely overestimated in the 20 points conditions due to the enormous increase in the standard errors of probability estimates. Third, it was shown that such an overestimation problem in the nonparametric method might be resolved properly when the standard error formulas of the normal method were used instead.

Development and testing of a modified SWAT model based on slope condition and precipitation intensity

Calibration, validation, parameter indentifiability and uncertainty analysis of a 2 – parameter parsimonious monthly rainfall-runoff model in two catchments in Zimbabwe

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km2) to 6.1% (post-calibration at 3125 km2) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km2) was decreased to 15.4% (post-calibration at 3125 km2). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.

Existing research on saltwater intrusion faces key challenges, including limited accuracy and difficulty in long-term prediction. This study introduces a long memory-double autoregressive (LMDAR) model based on fractional differencing, designed for daily prediction of hours exceeding the chloride threshold. Four salinity monitoring stations in the Pearl River Estuary were selected as case study stations. The model's performance was evaluated and compared with the double autoregressive (DAR) model, the long short-term memory (LSTM) model, and the gated recurrent unit (GRU) model using Nash–Sutcliffe efficiency (NSE), mean absolute error, and percent bias across different lead times. The results indicate that in short-term predictions (1 and 3 days), the prediction accuracy of the LMDAR, LSTM, and GRU models is comparable, with NSE values exceeding 0.7 at all four example stations and reaching up to 0.93. In long-term predictions (7, 15, and 30 days), the LMDAR model consistently achieved NSE values above 0.51 across the four stations, with the highest reaching 0.82. The LMDAR thus proves to be an effective tool for predicting hours exceeding the chloride threshold, demonstrating strong short-term performance and reliable long-term prediction capability.

In patients with heart failure, atrial septal defect (ASD) closure has a risk of inducing life-threatening acute pulmonary edema. The objective of this study was to develop a novel framework for quantitative prediction of hemodynamics after ASD closure. The generalized circulatory equilibrium comprises right and left cardiac output (CO) curves and pulmonary and systemic venous return surfaces. We incorporated ASD into the framework of circulatory equilibrium by representing ASD shunt flow (QASD) by the difference between pulmonary flow (QP) and systemic flow (QS). To examine the accuracy of prediction, we created ASD in six dogs. Four weeks after ASD creation, we measured left atrial pressure (PLA), right atrial pressure (PRA), QP, and Qs before and after ASD balloon occlusion. We then predicted postocclusion hemodynamics from measured preocclusion hemodynamics. Finally, we numerically simulated hemodynamics under various ASD diameters while changing left and right ventricular function. Predicted postocclusion PLA, PRA, and QS from preocclusion hemodynamics matched well with those measured [PLA: coefficient of determination (r2) = 0.96, standard error of estimate (SEE) = 0.89 mmHg, PRA: r2 = 0.98, SEE = 0.26 mmHg, QS: r2 = 0.97, SEE = 5.6 mL·min-1·kg-1]. A simulation study demonstrated that ASD closure increases the risk of pulmonary edema in patients with impaired left ventricular function and normal right ventricular function, indicating the importance of evaluation for the balance between right and left ventricular function. ASD shunt incorporated into the generalized circulatory equilibrium accurately predicted hemodynamics after ASD closure, which would facilitate safety management of ASD closure.NEW & NOTEWORTHY We developed a framework to predict the impact of atrial septal defect (ASD) closure on hemodynamics by incorporating ASD shunt flow into the framework of circulatory equilibrium. The proposed framework accurately predicted hemodynamics after ASD closure. Patient-specific prediction of hemodynamics may be useful for safety management of ASD closure.

Conceptual rainfall-runoff models have been experiencing many advancements in recent decades. These include improvements in model structure and wider calibration options. More complex model structure allows not only long-term modeling but also those at sub-hourly. Daily runoff volume, for instance, can be calculated at disaggregated timescales. Besides, the incorporation of sub-hourly timescales into continuous simulation models means that these models can now be used to estimate instantaneous flows. Nevertheless, the impact of time resolutions on modeling performance is not clear. Some past studies showed that the impact of timescales on model performance varied from almost no different to significant. While some studies showed higher time resolutions produced better results, the opposite has also been observed in few other studies. In relation to the calibration options, non-statistical measures have also been introduced in addition to the statistical ones. Parafield Drain (PD) is a water harvesting and reuse scheme involved in the Waterproofing Northern Adelaide (WNA) Project. In order to develop a decision support system (DSS) for PD which includes real- time rainfall-runoff modeling, it is necessary to select the best modeling timescale in terms of model performance. The present study investigates how model performance at PD modeled by WaterCress was affected by modeling time resolutions. In particular, two modeling outputs, i.e. runoff volume and peak discharge, were subjected to the assessment. Daily runoff volume assessment was based on rainfall data at two timescales, 30-min and daily, while flood peaks were estimated at 30-min and 1-h. Parameters of the former were calibrated and validated against 20 months of historic streamflow data, while the latter compared against two historical flood peaks. In relation to the model performance assessment, WaterCress provides 4 performance measures of which three of them are statistical: coefficient of determination (R 2 ), Nash-Sutcliffe Efficiency (NSE) and Standard Error of Estimate (SEE); and another non-statistical measure: Percentage Volumetric Difference (VD). In runoff modeling calibration stage, it was found that not every selected performance measures were improved. For example, VD was getting worse whenever the other measures were getting better and so forth. Assessing the model performance was therefore considered to be sufficient by considering multi-objective calibration, one from each statistical and non-statistical measures. Furthermore, a minimum of 9-month streamflow data was required to obtain calibration which was insensitive to the data length. Results also showed that higher time resolution produced slightly better prediction in runoff volume calculation and remarkably improved peak flow estimation.

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient ( $$\sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ ), differences of circular vertical and horizontal $$\sigma ^{\mathrm{o}} \, (\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}})$$ from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height ( $${\hbox {RMS}}_{\mathrm{height}}$$ ). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., $$\sigma ^{\mathrm{o}}$$ . Near surface SM measurements were related to $$\sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ , $$\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ derived using 5.35 GHz (C-band) image of RISAT-1 and $${\hbox {RMS}}_{\mathrm{height}}$$ . The roughness component derived in terms of $${\hbox {RMS}}_{\mathrm{height}}$$ showed a good positive correlation with $$\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}} \, (R^{2} = 0.65)$$ . By considering all the major influencing factors ( $$\sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ , $$\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ , and $${\hbox {RMS}}_{\mathrm{height}}$$ ), an SEM was developed where SM (volumetric) predicted values depend on $$\sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ , $$\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}$$ , and $${\hbox {RMS}}_{\mathrm{height}}$$ . This SEM showed $$R^{2}$$ of 0.87 and adjusted $$R^{2}$$ of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement ( $${\hbox {SM}}_{\mathrm{Observed}}$$ ) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 ( $${\approx } 1$$ ), index of agreement (d) = 1, coefficient of determination $$(R^{2}) = 0.87$$ , mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences $$({\hbox {S}}_{\mathrm{d}}^{2}) = 0.004$$ . The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on $$\sigma ^{\mathrm{o}}$$ . By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

In most developing countries, biophysical data are scarce, which hinders evidence-based watershed planning and management. To use the scarce data for resource development applications, special techniques are required. Thus, the primary goal of this study was to estimate sediment yield and identify erosion hotspot areas of the Andasa watershed with limited sediment concentration records. The hydrological simulation used meteorological, hydrological, suspended sediment concentration, 12.5 m Digital Elevation Model (DEM), 250 m resolution African Soil Information Service (AfSIS) soil, and 30 m resolution land-cover data. Using the limited sediment concentration data, a sediment rating curve was developed to estimate the sediment yield from discharge. The physical-based Soil and Water Assessment Tool (SWAT) model was employed to simulate streamflow and sediment yield in a monthly time step. The result shows that SWAT predicted streamflow with a coefficient of determination (R2) of 0.88 and 0.81, Nash–Sutcliffe Efficiency (NSE) of 0.88 and 0.80, and percent of bias (PBIAS) of 6.4 and 9.9 during calibration and validation periods, respectively. Similarly, during calibration and validation, the model predicted the sediment yield with R2 of 0.79 and 0.71, NSE of 0.72 and 0.66, and PBIAS of 2.7 and −8.6, respectively. According to the calibrated model result in the period 1992–2020, the mean annual sediment yield of the watershed was estimated as 17.9 t ha−1yr−1. Spatially, around 22% of the Andassa watershed was severely eroded, and more than half of the watershed (55%) was moderately eroded. The remaining 23% of the watershed was free of erosion risk. Therefore, the findings suggests that applying the sediment rating curve equation, in conjunction with hydrological and sediment modeling, can be used to estimate sediment yield and identify erosion hotspot areas in data-scarce regions of the Upper Blue Nile Basin in particular, and the Ethiopian highlands in general with similar environmental settings.

Understanding natural processes, particularly the water cycle, is inherently challenging due to their unpredictable and complex nature. This complexity is especially pronounced when employing hydrological models, where simplifications introduce various uncertainties. Failing to acknowledge and address these uncertainties can introduce biases into the model outcomes, potentially influencing subsequent decision-making processes. This issue is particularly pertinent in the Indian Himalayan Regions, where significant contributions come from melting of snow and glaciers.  The uncertainties in both model inputs and structures are exacerbated in this region, which is further compounded by the scarcity of reliable data. Consequently, there is a critical need to systematically quantify the diverse sources of uncertainty to ensure accurate and reliable hydrological predictions. This study focuses on the snow-dominant Alaknanda basin within the Indian Himalayan Region (IHR), encompassing three gauging stations. The SWAT+ hydrological model and Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) framework (Trotter et al., 2022) are employed to assess parameter and model structure uncertainties, respectively. The SWAT+ model, calibrated with the Latin Hypercube Sampling (LHS) algorithm, achieved Nash-Sutcliffe Efficiency (NSE) values of 0.56, 0.79, and 0.61. Parameter uncertainty is further examined using diverse parameter sets generated through the LHS algorithm. Furthermore, with the application of 47 lumped conceptual models within MARRMoT framework, assessment of model structure uncertainty underscores the varying importance of processes, particularly snow storage, soil moisture, and routing storage in the study region. The findings reveal that the inclusion of additional storage components in the model leads to a decline in performance, accompanied by an increase in complexity and uncertainties. Notably, the study concludes that, for the investigated region, the contribution of parameter uncertainty surpasses that of model structure uncertainty. These insights emphasize the need for a nuanced understanding of both parameter and structural uncertainties to enhance the reliability of hydrological predictions in data-scarce and complex regions like the IHR.

افزایش انتشار گازهای گلخانه‌ای در سال‌های گذشته با افزایش دما همراه بوده است. تغییرات دما از جمله عوامل مهم و تاثیرگذار در اقلیم هر منطقه بوده و به عنوان یکی از پارامترهای مهم مطالعات اقلیم‌شناسی همواره مطرح می‌باشد. در این مطالعه، روند درجه حرارت هوای استان آذربایجان‌غربی در دو مقیاس ماهانه و سالانه با استفاده از ویرایش چهارم آزمون ناپارامتری من- کندال (MK4) مورد بررسی قرار گرفت. داده‌های مورد استفاده دمای متوسط 24 ایستگاه تبخیرسنجی، در دوره آماری 1392-1360 می‌باشند. به منظور تخمین شیب خط روند، از روش تخمین‌گر شیب سن استفاده شد. نتایج نشان داد که در مقیاس سالانه، 71 درصد از ایستگاه‌های مورد بررسی (17 ایستگاه از 24 ایستگاه منتخب)، روند افزایشی معنی‌دار را در سطح اطمینان 10 درصد تجربه کرده‌اند و فقط 7 ایستگاه‌ (29 درصد از ایستگاه‌های منتخب) افزایش معنی‌دار در سری‌های درجه حرارت هوا نداشتند. بیشترین نرخ افزایش دما در مقیاس سالانه مربوط به ایستگاه چهریق با شیب (°C/Year) 12/0 بود. در مقیاس ماهانه، تعداد ماه‌ها با روند افزایشی شش برابر تعداد ماه‌ها با روند کاهشی بود. در این بین ماه‌های بهمن و اسفند بیشترین تعداد ایستگاه با روند افزایشی را به خود اختصاص داده‌اند. همچنین با توجه به مقدار شیب سن محاسبه شده، استان آذربایجان غربی در هر سال به طور متوسط 05/0 درجه سانتی-گراد (65/1 درجه سانتی گراد در طول دوره آماری مورد مطالعه) افزایش را در دمای سالانه تجربه کرده است.

Modeling groundwater flow under chaotic urbanization constraints in Kinshasa Capital Region (D.R. Congo)

آزادسازی روی از خاک در اثر فعالیت‌های بیولوژیکی یکی از عوامل مهم کنترل کننده قابلیت استفاده زیستی روی است. هدف از انجام این پژوهش، بررسی تاثیر تلقیح باکتری های محرک رشد و قارچ میکوریز بر سینتیک آزادسازی روی در خاک آهکی با سطوح مختلف شوری در کشت ذرت بود. آزمایشی فاکتوریل در قالب طرح کاملا تصادفی با سه تکرار در شرایط گلخانه انجام شد. فاکتور اول شامل سطوح شوری خاک (0، 15 و 30 میلی‌اکی والان نمک در کیلوگرم خاک از منابع کلرید سدیم، کلسیم و منیزیم به ترتیب به صورت ترکیبی 1:2:3) و فاکتور دوم تلقیح میکروبی (بدون تلقیح، تلقیح با قارچ، تلقیح با باکتری، تلقیح همزمان باکتری و قارچ) بود. کاربرد همه تیمارهای میکروبی میزان آزادسازی اولیه روی را نسبت به تیمار شاهد افزایش دادند. به ترتیب بیشترین و کمترین آزادسازی اولیه روی در تیمار قارچ-باکتری و باکتری مشاهده شد. همچنین با کاربرد همه تیمارهای میکروبی سرعت رهاسازی روی نسبت به تیمار شاهد کاهش یافت و کمترین کاهش در تیمار قارچ مشاهده شد. به طور کلی افزایش سطوح شوری سبب افزایش آزادسازی اولیه روی و کاهش سرعت آزادسازی روی شد. همچنین شکل محلول و تبادلی بیشترین تاثیر را در کنترل آزادسازی روی داشت. نتایج نشان داد که مدل‌های الوویچ ساده شده، سرعت دو ثابته و پخشیدگی پارابولیک توصیف خوبی را از آزادسازی روی نشان دادند. بر اساس بیشترین ضریب تبیین و کمترین خطای استاندارد، برترین مدل، الوویچ ساده شده تعیین شد، بنابراین به نظر می رسد که مکانیسم اصلی کنترل کننده آزادسازی روی، در خاک مورد آزمایش پدیده پخشیدگی باشد.

Attempts have been made to correlate different instability indices among themselves statistically. The study reveals that the Showalter Stability Index (SI) has moderate to good correlations with different instability indices except Dew-point Index (DPI), Vertical Total Index (VT), Modified Vertical Total Index (MVT) and Modified K-Index (MK). Most of the correlations co-efficient are found to be significant up to 99% level of significance except Dry Instability Index (DII), which has correlation with SI up to 95% level of significance. Lifted Index (LI) has moderate to good correlation with different instability indices except DII, K-Index (KI) and MVT. Most of the correlations co-efficient are significant up to 99% level of significance except VT, SWEAT Index (SWI) and MKI, which have correlation with LI up to 95% level of significance. Unmodified instability indices have moderate to strong correlation with the corresponding modified instability indices, having 99% level of significance. The correlation co-efficient of VT and MVT, SWI and Modified SWEAT Index (MSWI), and KI and MKI are comparatively large. Standard errors of estimate are small in almost all the cases except a few. The regression equations obtained are likely to be helpful in the computation of different instability indices.

Empirical approaches in prediction of reservoir sediment distribution—An experience of 57 reservoirs in the USA and India