Analysis of streamflow variability and trends in the Meta River, Colombia

The aim of this research is the detection and analysis of existing trends in the Meta River, Colombia, based on the streamflow records from seven gauging stations in its main course, for the period between June 1983 to July 2019. The Meta River is one of the principal branches of the Orinoco River, and it has a high environmental and economic value for this South American country. The methods employed for the trend detection and quantification were the Mann–Kendall (MK) test, the modified MK (MMK) test, and the Sen’s slope (SS) estimator. Statistically significant trends (at a 95% level of confidence) were detected in more than 30% of the 105 evaluated datasets. The results from the MK test indicate the presence of statistically significant downward trends in the upstream stations and upward trends in the downstream stations, with the latter presenting steep positive slopes. The findings of this study are valuable assets for water resources management and sustainable planning in the Meta River Basin

A short-term reservoir operation model for multicrop irrigation

An integrated model is developed for short-term yearly reservoir operation for irrigation of multiple crops. The model optimizes a measure of annual crop production, starting from the current period in real time. Reservoir storage at the begining of a period, inflow during the previous period, crop soil moisture values and crop production already achieved up to the beginning of the period are used as inputs to the model. The solution specifies the reservoir release and optimal irrigation allocations to individual crops during an intra-seasonal period. The model overcomes some of the limitations of an earlier model developed by Mujumdar & Ramesh (1997) by replacing the two dynamic programming (DP) formulations with a single linear programming (LP) formulation. Application of the model is studied through a case study in India

A short-term reservoir operation model for multicrop irrigation

An integrated model is developed for short-term yearly reservoir operation for irrigation of multiple crops. The model optimizes a measure of annual crop production, starting from the current period in real time. Reservoir storage at the beginning of a period, inflow during the previous period, crop soil moisture values and crop production already achieved up to the beginning of the period are used as inputs to the model. The solution specifies the reservoir release and optimal irrigation allocations to individual crops during an intra-seasonal period. The model overcomes some of the limitations of an earlier model developed by Mujumdar & Ramesh (1997) by replacing the two Dynamic Programming (DP) formulations with a single Linear Programming (LP) formulation. Application of the model is studied through a case study in India

Data mining application in assessment of weather-based influent scenarios for a WWTP : getting the most out of plant historical data

Since the introduction of environmental legislations and directives, the impact of combined sewer overflows (CSO) on receiving water bodies has become a priority concern in water and wastewater treatment industry. Time-consuming and expensive local sampling and monitoring campaigns are usually carried out to estimate the characteristic flow and pollutant concentrations of CSO water. This study focuses on estimating the frequency and duration of wet-weather events and their impacts on influent flow and wastewater characteristics of the largest Italian wastewater treatment plant (WWTP) located in Castiglione Torinese. Eight years (viz. 2009–2016) of historical data in addition to arithmetic mean daily precipitation rates (PI) of the plant catchment area are elaborated. Relationships between PI and volumetric influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4), and total suspended solids (TSS) are investigated. A time series data mining (TSDM) method is implemented with MATLAB computing package for segmentation of time series by use of a sliding window algorithm (SWA) to partition the available records associated with wet and dry weather events. According to the TSDM results, a case-specific wet-weather definition is proposed for the Castiglione Torinese WWTP. Two significant weather-based influent scenarios are assessed by kernel density estimation. The results confirm that the method suggested within this study based on plant routinely collected data can be used for planning the emergency response and long-term preparedness for extreme climate conditions in a WWTP. Implementing the obtained results in dynamic process simulation models can improve the plant operational efficiency in managing the fluctuating loads

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies

Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique

This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009-2016) of historical influent data namely influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (PI) from the plant catchment area, are evaluated in this study. Following the outlier removal and missing-data imputation, five influent climate-based scenarios are identified by K-means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10-40% energy saving can be achieved while meeting effluent requirements

Spatiotemporal variation of water balance components in Mashhad catchment, Iran: Investigating the impact of changes in climatic data and land use

The research aims to investigate the spatiotemporal changes in water balance components and distinguish the relative impacts of climatic data and land-use on groundwater levels in northeastern Iran. This investigation employs the WetSpass-M model to estimate water balance and the Mann-Kendall test alongside Sen's slope estimator to evaluate the trend. The study also assesses mean annual water balance components, considering diverse combinations of land use and soil. The findings offer a hydrological insight revealing that 14% of precipitation results in runoff, 29% of that recharging the aquifer; the remaining portion is lost through evapotranspiration. The trends in precipitation and simulated water components are not significant but a significant downward trend in groundwater is observed beyond a specific point in time. Based on this outcome, as well as the analysis of land-use changes, it was speculated that human activities in this fast-developing region might be implicated in the decline in groundwater levels. Analysis of water balance components in various soil and land-use combinations indicates that evapotranspiration exhibits greater variability within the land-cover class, while recharge is more influenced by soil texture. These findings enhance our understanding of identifying potential sites for artificial recharge and determining sustainable groundwater withdrawals based on spatiotemporal recharge patterns