Water Resource Planning Research Articles

Spatial–Temporal Water Balance Evaluation in the Nile Valley Upstream of the New Assiut Barrage, Egypt, Using WetSpass-M

The components of water balance (WBC) that involve precipitation, evapotranspiration, runoff, irrigation, and groundwater recharge are critical for understanding the hydrological cycle and water management of resources in semi-arid and arid areas. This paper assesses temporal and spatial distributions of surface runoff, actual evapotranspiration, and groundwater recharge upstream of the New Assiut Barrage (NAB) in the Nile Valley, Upper Egypt, using the WetSpass-M model for the period 2012–2020. Moreover, this study evaluates the effect of land cover/land use (LULC) alterations in the study period on the WBC of the NAB. The data provided as input for the WetSpass-M model in the structure of raster maps using the Arc-GIS tool. Monthly meteorological factors (e.g., temperature, rainfall, and wind speed), a digital elevation model (DEM), slope, land cover, irrigation cover, a soil map, and depth to groundwater are included. The long-term temporal and spatial mean monthly irrigation and precipitation (127 mm) is distributed as 49% (62 mm) actual evapotranspiration, 15% (19 mm) groundwater recharge, and 36% (46 mm) surface runoff. The replacement of cropland by built-up areas was recognized as the primary factor responsible for the major decrease in groundwater, an increase in evapotranspiration and an increase in surface runoff between LCLU in 2012 and 2020. The integration of the WetSpass model with GIS has shown its effectiveness as a powerful approach for assessing WBC. Results were more accurate and reliable when hydrological modeling and spatial analysis were combined. The results of this research can help make well-informed decisions about land use planning and sustainable management of water resources in the upstream area of the NAB.

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran).

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and - 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to - 344, - 332.8, and - 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

Improving the Applicability of Lumped Hydrological Models by Integrating the Generalized Complementary Relationship

AbstractLumped hydrological models (LHMs) are indispensable for water resource planning and environmental studies due to simple structures and robust performances. LHMs commonly focus on runoff processes with crude representations for other hydrological processes, such as evapotranspiration (E). Therefore, these models may yield unrealistic water balance partitioning. The challenge is to enhance the LHMs performance while retaining simplicity. The generalized complementary relationship (GCR) is a simple and robust method for estimating E. This study attempted to incorporate GCR into four widely used LHMs (Australian water balance model, GR2M, SIMHYD, and TANK) to test whether the integrated models (GCR‐LHMs) can improve runoff simulation at little cost to the model structure and data requirement. Original LHMs and integrated GCR‐LHMs were tested in 2112 catchments over various climatic conditions. Results show that the GCR‐LHMs outperform original LHMs in most catchments (77.7 ± 5.0%). In addition, the number of catchments that GCR‐LHMs have qualified performance (i.e., Kling‐Gupta coefficient [KGE] more than 0.5) increased by 10.7 ± 3.6% compared with LHMs. The performance of original and integrated models is dependent on the aridity index and normalized vegetation index. However, the improvement in model performance is less catchment characteristics dependent. These results indicate that incorporating GCR into the LHMs improves the model performance under different climatic and vegetation conditions and justifies the integration. GCR integration with LHMs can improve runoff estimation ability (with higher KGE and R‐Square) while retaining model simplicity and readily available input. These findings are valuable for improving the applicability and accuracy of LHMs.

Medium-term water consumption forecasting based on deep neural networks

Water consumption forecasting is an essential tool for water management, as it allows for efficient planning and allocation of water resources, an undervalued but indispensable resource for all living beings. With the increasing demand for accurate and timely water forecasting, traditional forecasting methods are proving to be insufficient. Deep learning techniques, which have shown remarkable performance in a wide range of applications, offer a promising approach to address the challenges of water consumption forecasting. In this work, the use of deep learning models for medium-term water consumption forecasting of residential areas is explored. A deep feed-forward neural network is developed to predict water consumption of a company’s customers for the next quarter. First, customers are grouped according to their consumption as these customers include both household consumers and special consumers such as public swimming pools, sports halls or small industries. Then, a deep feed-forward neural network is designed for household customers by obtaining the optimal values for those hyperparameters that have a great influence on the network performance. Results are reported using a real-world dataset composed of the water consumption from 1999 to 2015 on a quarterly basis, corresponding to 3262 clients of a water supply company. Finally, the proposed algorithm is evaluated by comparing it with other reference algorithms including an LSTM network.

Assessing Groundwater Recharge in the Wabe River Catchment, Central Ethiopia, through a GIS-Based Distributed Water Balance Model

The utilization of groundwater has emerged as an indispensable asset in facilitating economic advancement, preserving ecological integrity, and responding to the challenges posed by climate change, especially in regions characterized by aridity and semi-aridity. The sustainable management of water resources requires an assessment of the geographical and temporal patterns of groundwater recharge. The present study employed the GIS-based WetSpass-M model to model the water balance components by utilizing hydro-meteorological and biophysical data from the Wabe catchment, which spans an area of 1840 km2 in central Ethiopia, for a long time. The objective of this study was to assess the long-term average annual and seasonal groundwater recharge for the catchment area utilizing the WetSpass-M model. The input data were collected through remote sensing data and surveys in the field. The model was employed to gain insights into the process of groundwater recharge in a particular region and to facilitate effective management, prudent utilization, and sustainable planning of water resources in the long run. Water balance components were estimated using seasonal fluctuations in evapotranspiration, surface runoff, and groundwater recharge. The Wabe catchment’s summer, winter, and mean long-term yearly groundwater recharge were determined to be 125.5 mm, 78.98 mm, and 204.51 mm, respectively. The model indicates that summer seasons account for 86.5% of the mean annual precipitation, while winter seasons account for 13.5%. On the other hand, the groundwater system percolates 14.8% of the total annual rainfall (1374.26 mm). While evapotranspiration accounts for 51% of total precipitation and surface runoff accounts for 34.1%, the Wabe catchment’s mean annual evapotranspiration and surface runoff values are simulated at 701.11 mm and 485.58 mm, respectively. The findings suggest the use of the WetSpass-M model to precisely calculate the water balance components within the Wabe catchment.

Farming System Choice Is Key to Preserving Surface Water Quality in Agricultural Watersheds

Despite numerous published literature on the impacts of agriculture on water quality, knowledge gaps persist regarding which farming systems are of most concern for their impact on water quality, which would allow water resource planners to better target water management efforts. Seeking to understand how these relationships vary across different farming systems, we used data on water quality status in watersheds of an agricultural region in southern Portugal and overlaid it with a map of farming systems for the same region provided by a previous study. By intersecting both data layers, we characterized the areal shares of the farming systems in each watershed and inspected how these shares relate to water quality status using logistic regression. The results show that the impact of agriculture on water quality is primarily related to specific farming systems. We believe this type of information can be of significant interest to agricultural planners and policymakers interested in meeting water quality standards, and we conclude this study by suggesting innovative policy options based on payments to farmers operating selected farming systems as a cost-effective way to reconcile agricultural and environmental policy objectives.

Rating curve development and uncertainty analysis in mountainous watersheds for informed hydrology and resource management

Accurate measurement of continuous stream discharge poses both excitement and challenges for hydrologists and water resource planners, particularly in mountainous watersheds. This study centers on the development of rating curves utilizing the power law at three headwaters of the lesser Himalayas—Aglar, Paligaad, and Balganga—through the installation of water level recorders for stage measurement and salt dilution for discharge measurement from 2014 to 2016. The stream stage–discharge relationship, crucially known as the rating curve, is susceptible to numerous factors in mountainous watersheds that are often challenging to comprehend or quantify. Despite significant errors introduced during the rating curve development, such as stemming from observations, modeling, and parameterization, they are frequently overlooked. In this study, acknowledging the inherent uncertainty, we employ the maximum-likelihood method to assess uncertainty in the developed rating curve. Our findings reveal substantial inconsistency in the stage–discharge relationship, particularly during high flows. A novel contribution of this study is introducing a weighing factor concept that correlates uncertainty with the morphological parameters of the watershed. The higher value of the weighting factor in Paligaad (0.37) as compared to Balganga (0.35) and less in the case of Aglar (0.27) will have more uncertainty. The authors contend that precise rating curves and comprehensive uncertainty analyses can mitigate construction costs, foster robust decision-making, and enhance the perceived credibility of decisions in hydrology and water resource management.

Trend Analysis of Drought Severity in Southeast Region of Türkiye

The study of drought, one of the many problems caused by climate change, enables the planning of water use and water resources. The increase or decrease in drought severity, which is one of the drought parameters, is an important source of information in terms of predetermining the consequences that may arise due to drought. In this study, drought analysis was conducted for 1-, 3-, 6-, 9- and 12-month time periods with the Standard Precipitation Index (SPI) using long-term precipitation data of four stations in the Tigris River basin from 1960 to 2022. Drought duration and severity series were obtained from drought index values, and the trends of drought severity series were calculated with Mann-Kendall (MK) and Innovative Trend Analysis (ITA) tests. According to the results, the ITA test was found to be more sensitive in detecting trends in drought severity series compared to the MK test. According to the MK test, drought did not show a significant increase or decrease. On the other hand, according to the ITA test, drought increased at 1- and 12-month scales at Siirt and Batman stations, respectively, and at 1- and 12-month scales at Diyarbakır station. Drought increased in all time periods at Mardin station.

Longitudinal dissolved oxygen patterns in Atlantic salmon aquaculture sites in British Columbia, Canada

Dissolved oxygen (DO), an important water-quality parameter required to support aquatic life, is a critical factor for determining the general biological health of the aquatic ecosystem, and the concentration of DO is a critical factor in determining salmon growth and welfare. This study used longitudinal DO concentration, recorded hourly from 21 aquaculture sites, each with loggers in three separate cages, in four areas in British Columbia, Canada, between 2015 and 2017. The measurements were evaluated based on the recommended DO concentrations for protection of salmonids from hypoxia. Using a two-stage time-series analysis, we described variations in DO concentrations measured over the study period and their associations with environmental factors. Based on the water quality criteria for DO concentration, 42.3, 56.5, and 1.2% of the hourly DO data from the overall 21 aquaculture sites were classified as ‘optimal’, ‘sub-optimal’, and ‘stressed’, respectively. The frequency of hypoxic episodes differed substantially among seasons, aquaculture sites and even among cages within sites. The effects of environmental variables on DO concentration had markedly different patterns depending on the season. Significant associations with DO concentrations were observed for temperatures in the summer and winter months, and both wind direction and remotely-sensed estimated absorption from phytoplankton [aph(443)] in the winter months. The time-series regression model results showed overall (year-round) associations of temperature, wind speed, and aph(443) with DO concentrations. Describing DO measurements at these aquaculture sites provided an understanding of how much they deviated from the recommended DO concentrations, as well as provide baseline information for future water resource planning, including continued and improved water quality monitoring in aquaculture areas.

Improving urban water demand forecast using conformal prediction-based hybrid machine learning models

This paper presents a probabilistic forecasting method that predicts the future water consumption patterns, taking into account the inherent uncertainty in the system. The proposed method leverages statistical techniques to estimate the probability distribution of future water demand scenarios. The findings of this study will provide decision-makers with a range of possible alternatives that facilitate better planning and management of water resources. We developed a conformal prediction-based hybrid demand forecast model, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) while comparing it with other machine learning models for probabilistic hourly water demand forecasting. Additionally, we address crucial considerations when implementing a probabilistic forecasting system, including selecting appropriate data and choosing model parameters. The performance of the proposed model is validated for probabilistic water demand forecasting in real-world settings. Results indicate noteworthy improvement in deterministic and probabilistic predictions by 10 % and 26.7 %, respectively. The findings underscore the potential benefits of this approach for improved decision-making and resource management. The results also demonstrate that the proposed hybrid model outperforms the compared machine learning models with better prediction performance and increased robustness for handling prediction uncertainties.

Study On Flood Management and Analysis Using Geographical Information Systems On Godavari, Konaseema District, India

The Konasema area in Andhra Pradesh, India is prone to flooding due to coastal storms, storm surges, and river overflows. Flooding can be dangerous and pose a threat to human life and property. Floods are unpredictable and can happen suddenly, causing significant damage. Therefore, planning for flood management is essential for rescuing flood victims, resolving flood issues, and implementing preventive measures. In recent years, flooding has become more frequent in the Konaseema area, especially in the Godavari river's and near surrounding areas. Heavy floods from the Godavari River have been caused severe flooding in nearby konaseema villages. Flood management by water resource planners using GIS (Geographic Information System) developed land use and land cover maps, Flood zone maps in the study region. This study aims to analyse impact of Godavari River floods in Konaseema and East Godavari district Andhra Pradesh, India. DEM (Digital Elevation Model) is generated and prepared various thematic maps of Flood prone regions. The GIS generated flood impacted maps are analyzed and these maps will help to rescue the people from flood regions and migrate them in time to the safe zones.

Modelagem integrada de qualidade e quantidade para gestão de recursos hídricos: estudo de caso na Bacia do Alto Paranapanema

ABSTRACT This study introduces an integrated model for water resources planning and management that accounts for both the quantity and quality of water in watersheds and reservoirs. Therefore, it provides a comprehensive approach for better understanding and assessing water systems. The model facilitates analyses of the spatial and temporal dynamics of hydrological processes, pollutant transport, and the behavior of rivers and reservoirs. The study evaluated pollutant load transport in the Upper Paranapanema Basin by applying the Soil Moisture Accounting Procedure (SMAP) model, Muskingum method, Streeter-Phelps buildup/washoff model, and zero-dimensional (0D) models to estimate inflows to the Jurumirim reservoir. A Python-based public library was developed to model all hydrological processes. The model calibration yielded a NSE, KGE, PBIAS and RMSE result of 0.746, 0.778, 6.5% and 73.3 m 3 s − 1, respectively. Indicating a robust simulation that attests to the versatility and reliability of the proposed approach. The methodology outlined in this research enables comprehensive water quality simulation at basin scale, thereby serving as a tool for understanding water quantity and quality dynamics and support decision-making regarding water resource planning and management.

Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin

For flood control, hydropower operation, and agricultural planning, among other applications, flow discharge prediction is a critical first step toward the strong and dependable planning and management of water resources. Floods are destructive natural calamities that destroy human lives and infrastructure across the world. Development of effective flood forecasting and prediction models is critical for minimising deaths and mitigating damages. This study employs hybrid deep learning Long Short Term Memory (LSTM) algorithms like LSTM, Convolution LSTM (Conv-LSTM) and Convolutional Neural Network LSTM (CNN-LSTM) to predict likelihood flood events using daily precipitation, daily temperature and daily relative humidity from two flood-forecasting stations i.e., Champua (Baitarani River, Odisha) and Jarikela (Brahmani River, Odisha) over a 20-year period. The results show that CNN-LSTM performed best followed by Conv-LSTM and LSTM in terms of R2 = 0.98055, 0.96564, and 0.93244, RMSE = 19.137, 35.635, and 49.347, MAE = 18.372, 33.766, and 47.058, NSE = 0.971, 0.9517 and 0.9257 respectively. The findings support the claim that machine learning models and algorithms, in particular CNN-LSTM model, can be applied to flood forecasting with high accuracy, thereby enhancing water and hazard management.

Construction and Application of Intelligent Optimization Model of Urban Resource Management for Sustainable Development

Abstract In this paper, we first digitally design the urban management resource base and then construct a dynamic bargaining resource allocation model based on super-marginal analysis with corner point equilibrium and dynamic bargaining. In the context of sustainable development, an intelligent optimization model is used to analyze the problem of water resource allocation and optimize urban traffic infrastructure paths in order to achieve carbon reduction targets. Finally, six recommendations are made for optimizing and managing urban resources in the context of sustainable development. The results of the prioritization coefficients of each subarea are 0.186, 0.164, 0.153, 0.158, 0.153, 0.0769, and 0.111, respectively, and the results of the allocation of the optimal solution for the social objective, the optimal solution for the economic benefit, the optimal solution for the ecological benefit, and the optimal solution for the comprehensive benefit are very close to each other. In addition, the rates of water shortage for agricultural, industrial, and other uses. are 3.29%, 4.85%, 3.94%, 2.92%, and 3.87%, respectively. The variation range of the average passenger load factor and departure frequency under the carbon reduction target is between ±40%, the average vehicle area varies in the range of ±20%, and the variation range of the energy consumption factor and CO2 emission factor is -40%-0%. The intelligent optimization model for urban resource management proposed in this paper has practical significance for urban water resource planning and transportation structure optimization.

Unravelling seasonal dynamics: Inter-station correlations and hydrological assessments of Topľa river in the Bardejov basin

The study delves into the complex seasonal dynamics of water levels in the Topl'a River, strategically positioned within the Bardejov basin. Hydrological assessments, aided by the water-bearing coefficient, classify the hydrological situation into Wet, Normal, or Dry conditions, creating a robust framework for clustering water levels across different years. The measurement stations, strategically located in Bardejovská Dlhá Lúka (stream Kamenec), Bardejov (river Topľa), Kľušovská Zábava (stream Šibská voda), and Gerlachov (river Topľa), contribute unique insights. Notably, Gerlachov and Bardejov represent different locations of the same stream, while Šibská voda and Kamenec are spatially proximate to Bardejov, experiencing similar meteorological conditions. Results from statistical analyses showcase distinctive seasonality patterns at each location and how the correlation between these stations evolves across different hydrological conditions. Intriguingly, the findings reveal an insignificant spatial relationship of correlation between station locations and seasonality, challenging conventional expectations. The implications of these results for water management and resources are discussed, emphasizing the need for a nuanced understanding of seasonality dynamics in optimizing water resource planning.

An Introduction to Consumptive Use of Water in South Carolina

Effective water resource management requires understanding the supply of and the demand for water. In South Carolina, as in other places, water demand is often determined using total withdrawal volumes. However, the volume of water that is withdrawn can be significantly different from the volume that is actually consumed, which becomes unavailable for downstream uses. Water used for energy generation is commonly excluded from evaluations of total withdrawal volume because it is often assumed to be no or low consumptive use, meaning much of the withdrawn water is returned to the source and remains available for downstream uses. Additionally, energy production withdrawal volumes may be significantly higher than other sectors’ usage and make it difficult to further compare water use of other sectors. Consumptive use volumes are not readily available for South Carolina and can be challenging to determine. However, estimates of consumptive use could allow more meaningful comparisons between water use sectors’ impacts. The objective of this short communication is to briefly discuss data sources, outline two relatively simple methods for calculating consumptive use with available data, identify challenges and opportunities for additional research, and provide preliminary estimates of consumptive water use volumes per water use sectors in South Carolina. Expanded discussion of consumptive water use of thermoelectric energy generation is included due to the significant total water withdrawals and unique challenges with calculating consumptive use of this sector. These results inform water resource planning and identify additional research opportunities.

An integrated climate and water resource climate service prototype for long term water allocation in the Upper Yellow River region of China

Water Resourcing in China has historically been a complex issue requiring the ability to deal with regular floods, droughts and diverse water needs. Climate change represents another challenge to this sector, albeit one that is not traditionally considered by water managers. In this sector in China water management is predominantly based on historic, seasonal and annual forecast data while multi-annual and (multi-)decadal data are seldom used. In this paper, we present the co-development of a climate service prototype designed to provide water managers with insights into the impacts of climate change on the Upper Yellow River region for the next century. The paper is an outcome from our project that encouraged water resource planners and water resource managers to utilise long-term climate information to understand the uncertainties and the challenges our changing climate is likely to have in the region. Using an interdisciplinary team and adopting a user-centred, co-production approach, a prototype web-based data visualisation tool was developed. The development of the prototype was based on a design specification constructed from the findings of detailed interviews that allowed it to be developed and tested under SARS-CoV-2 pandemic restrictions that prevented the typical development process to be undertaken. The developed prototype presents climate information and communicates uncertainties regarding climate change in the remainder of the century through data sets that are typically used by the water sector in China in a simple, easy to understand style. Models that estimate river levels under different extraction scenarios and results about estimated river level and flow, and flood risk are also presented. The prototype was shown to be successful, as key messages relating to the impact of climate change and the challenges for water resource management could be effectively communicated through the tool interface. Practical implicationsUnderstanding the impacts of climate change on water resourcing is complicated and multifaceted. There is a need for better data about what water there is and how it is moving around between and within catchments. Estimates of past, present and future climate variables along with historical measurements of river flow can be used to help visualise some of the uncertainties and changes that may happen in the next 50 years. In addition, there is a need to understand changing water demands and water resource management practices. Current water resource management practices are based on historical conditions and assumptions that are less likely to hold true in a more variable and warmer climate. Communicating how future changes will impact future water resourcing is critical to water resources in a changing climate (Belcher et al. 2018).This research outlines the construction of a tool to visualise the impacts of climate change on water availability in part of China that is typically water scarce, using models developed using the Soil Water Assessment Toolkit (SWAT). A model of the Upper Yellow River (UYR) was developed to demonstrate the impact of climate change on river levels in the catchment based on climate variables. The rainfall-runoff model was based on climate predictions from the CMIP5 assessment HadGEM3-GC3.05 climate model and incorporated information about water resource allocations for different administrative regions of the catchment The general climate trend for the region is that it is expected to become significantly warmer. The total amount of precipitation is likely to be about the same, and yet it is expected that overall, the catchment will become significantly drier over time as winter shortens and summer lengthens. The outputs from the model reflect the changes in climate variables. The uncertainties were communicated via a Web based tool. Water resource managers in China helped to coproduce the tool by participating in workshops and providing feedback on prototypes. The workshops helped scientists and water resource managers to communicate about climate change impacts on water resources and water resource management.

The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

The application of artificial intelligence (AI) technologies is quickly expanding in water management. Additionally, the artificial neural network methodology has an advantage over traditional statistical approaches in that it does not need assumptions about the distribution of data and variables. These methods can be used if there is a large enough data collection and criteria relevant to the nature of the problem. Preprocessing data before utilizing it improves the performance of the AI model. Evaporation matters in water management, agriculture processes and soil science. It is critical to ensure proper estimation of evaporation losses for effective water resource planning and management particularly in drought-prone areas such as Adana. Artificial intelligence approaches can be applied successfully in evaporation calculation. In this research, we used the Standard scaler, power transformer, robust scaler quantile transformer (Uniform) and quantile transformer (Normal), and min-max scaler preprocessing techniques to preprocess the multilayer perceptron neural network (MLPNN). We also trained the MLPNN using unprocessed data and compared it to the results of the preprocessed model. In the setup of the model, daily temperature, pressure, wind, sunny hours, and humidity parameters covering the years 2018-2021 were presented as input to the MLPNN model. Consequently, we pinpoint that all preprocessing approaches produce better outcomes than unscaled. Although all models produced statistically high accuracy predictions according to statistical criteria, the MLPNN model established without transformation (test phase: r2: 0.93, NSE : 0.927, SMAPE: 10.77, RMSE: 1.2, MAE: 0.9) exhibited the lowest accuracy. The evaporation prediction model that was developed using the MLPNN-based standard scalar optimization algorithm exhibited the highest level of accuracy (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.93, RMSE: 0.68, MAE: 0.48). Power Transformer (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.81, RMSE: 0.67, MAE: 0.49) showed second-degree promising results. It can be concluded from these results that the estimation of meteorological variables requires the scaling and presentation of data in a uniform structure. Therefore, improving efficiency and productivity in water management or agricultural processes can be enhanced by making more accurate evaporation estimates.

Maximum energy entropy: A novel signal preprocessing approach for data-driven monthly streamflow forecasting

In recent years, the application of Data-Driven Models (DDMs) in ecological studies has garnered significant attention due to their capacity to accurately simulate complex hydrological processes. These models have proven invaluable in comprehending and predicting natural phenomena. However, to achieve improved outcomes, certain additive components such as signal analysis models (SAM) and input variable selections (IVS) are necessary. SAMs unveil hidden characteristics within time series data, while IVS prevents the utilization of inappropriate input data. In the realm of ecological research, understanding these patterns is pivotal for grasping the ecological implications of streamflow dynamics and guiding effective management decisions. Addressing the need for more precise streamflow forecasting, this study proposes a novel SAM called “Maximum Energy Entropy (MEE)” to forecast monthly streamflow in the Ajichai basin, located in northwestern Iran. A comparative analysis was conducted, pitting MEE against well-known methods such as Discreet Wavelet (DW) and Discreet Wavelet-Entropy (DWE), ultimately demonstrating the superiority of MEE. The results showcased the superior performance of our proposed method, with an NSE value of 0.72, compared to DW (NSE value of 0.68) and DWE (NSE value of 0.68). Furthermore, MEE exhibited greater reliability, boasting a lower Standard Deviation value of 0.13 compared to DW (0.26) and DWE (0.19). The utilization of MEE equips researchers and decision-makers with more accurate predictions, facilitating well-informed ecological management and water resource planning. To further evaluate MEE's accuracy using various DDMs, we integrated MEE with Artificial Neural Network (ANN) and Genetic Programming (GP). Additionally, GP served as an IVS method for selecting appropriate input variables. Ultimately, the combination of MEE and GP within the ANN forecasting model (MEE-GP-ANN) yielded the most favorable results.

Future perspective of water budget in the event of three scenarios in Afghanistan using the WEAP program

In this study, WEAP (Water Evaluation and Planning System) software was used for the water resources management and water budget calculation of the Balkh River Basin, the sub-basin of the Northern River Basin in Afghanistan. The water resources in the basin and 10 demand sites (irrigation, drinking water, etc.) were introduced to the software, and precipitation, evaporation, runoff, etc., were obtained from stream gauging stations for long years (2010–2021). Hydrological data and information on population, irrigation lands, and drinking water use were entered. Scenario-1 (current situation) was created by entering the existing data in the created model. Subsequently, the long-term monthly average maximum and minimum values of precipitation, evaporation, runoff, and similar values were added to the current scenario. Forecast results between the years (2022–2050) were created with Scenario-2 (optimistic case) and Scenario-3 (pessimistic case), and analyzes were carried out in three different situations, and the results were examined. According to the analyzes, the current water potential found by the inclusion of surface water and groundwater from the Mezar-i-Sharif Region in the Balkh River Basin is 4747 million m3 annually; it has been determined that this value will be approximately 9741 million m3 in 2050 in the optimistic scenario and 1811 million m3 in the pessimistic scenario. It has been determined that the water need, which cannot be met as 29,394 million m3 in the current situation, approximately 14103 m3 in the optimistic scenario, and 49,979 million m3 in the pessimistic scenario in 2050. While the current met need of the Balkh River Basin is 7.81 % with losses, it has been calculated as 27.14 % in an optimistic situation and 1.71 % in a pessimistic situation for 2050. While the current met needs of the Basin, excluding losses, is 12 %, it is calculated as 33.9 % in the optimistic situation and 3.1 % in the pessimistic situation for 2050. It is understood that Afghanistan's water resources should be handled with an integrated water resources management (IWRM) approach, and future water resource planning should be made by considering human needs, environmental impact, and government visions. Water resources should be planned most efficiently by controlling the amount of undocumented wells, drilling, and detecting the losses and leakages in the existing systems with certain methods.