Water Resources Management Strategies Research Articles

Evaluating a finer resolution global hydrological model’s simulation of discharge in four West-African river basins

Performance evaluation of hydrological models enables their consolidation, thereby allowing for the evaluation of water resource conservation approaches. This research aims to evaluate the performance of a finer resolution version (5 arcmin) of the PCRaster Global Water Balance (PCR-GLOBWB) for discharge estimation, in four basins in data-scarce West Africa; the Niger, Komadugu-Yobe, Jama'are, and Ogun. At the Ogun, discharge simulation was validated in a proxy basin, Oueme, which is hydrologically comparable. The model performance was evaluated using Nash–Sutcliffe efficiency (NSE), coefficient of determination (r2), Kling–Gupta efficiency (KGE), RMSE—observations standard deviation ratio (RSR), percent bias (PBIAS) and visual plots. PCR-GLOBWB was found to be suitable in all four basins but yielded better performance at three of the basins; the Niger, Jama'are, and Komadugu-Yobe (NSE, KGE, and r2 above 0.7) compared to the Ogun basin where a proxy validation approach was followed. Results at the Ogun underlined the importance of measured data in hydrological studies. Still, model performance was satisfactory in the Ogun. PCR-GLOBWB performances across the four basins, in the area, validate its reliability as a tool applicable for water resources management strategies and further investigation of impacts of climate variations on river dynamics.

Dynamics of a stochastic eutrophication-chemostat model with impulsive dredging and pulse inputting on environmental toxicant

In this paper, we present a stochastic eutrophication-chemostat model with impulsive dredging and pulse inputting on environmental toxicant. The sufficient condition for the extinction of microorganisms is obtained. The sufficient condition for the investigated system with unique ergodic stationary distribution is also obtained. The results show that the stochastic noise, impulsive dredging, and pulse input on the environmental toxicant play important roles in the extinction of microorganisms. The results also indicate the effective and reliable controlling strategy for water resource management. Finally, numerical simulations are employed to illustrate our results.

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square(RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.

Forecasting Urban Water Demand Using Cellular Automata

Associating the dynamic spatial modeling based on the theory of cellular automata with remote sensing and geoprocessing technologies, this article analyzes what would be the per capita consumption behavior of Fortaleza-CE, located in the Northeast of Brazil, in 2017, had there not been a period of water scarcity between 2013 and 2017, and estimates the future urban water demand for the years 2021 and 2025. The weight of evidence method was applied to produce a transition probability map, that shows which areas will be more subject to consumption class change. For that, micro-measured water consumption data from 2009 and 2013 were used. The model was validated by the evaluation of diffuse similarity indices. A high level of similarity was found between the simulated and observed data (0.99). Future scenarios indicated an increase in water demand of 6.45% and 10.16% for 2021 and 2025, respectively, compared to 2017. The simulated annual growth rate was 1.27%. The expected results of urban water consumption for the years 2021 and 2025 are essential for local water resources management professionals and scientists, because, based on our results, these professionals will be able to outline future water resource management strategies.

Goal programming model for optimal water allocation of limited resources under increasing demands

A multi-objective model has been applied for optimal allocation of water from limited resources to meet increasing demands in multiple sectors. The study region is Saudi Arabia, while the time span is from 2020 to 2050 with interval of 10 years. The available water resources in Saudi Arabia are groundwater (GW), surface water (SW), desalinated water (DW) and treated wastewater (TWW), whereas the users are domestic, agricultural and industrial sectors. The goal programming technique has been used to formulate the model with multiple objectives. These objectives are (1) water demands satisfaction; (2) water quality control; (3) maximizing allocation of SW and TWW; and (4) minimizing extraction of GW, overproduction of DW and overall cost of water use. Allocations of GW, SW and DW in domestic sector are 508.4, 225.7 and 2174.2 MCM, respectively, in 2020, which are projected to be 585.3, 349.1 and 4494.8 MCM, respectively, in 2050. From 2020 to 2050, contribution of GW and SW in agricultural sector may reduce from 10,283.5 and 218.7 MCM to 8753 and 95.2 MCM, respectively, while this reduction is recovered by increasing contribution of TWW from 2291.9 in 2020 to 3945.8 MCM in 2050. During this period, contribution of GW in industrial sector shows increasing rate of approximately 171 MCM per year. Water quality is controlled through controlling the level of total dissolved solids to the standards. Production of DW and TWW has to be increased in order to meet the future needs. The total cost of using water is estimated as 13 billion US$ in 2020, which is predicted to be 22 billion US$ in 2050. This study attempted to suggest the possible scenarios to satisfy increasing water demands from limited resources in different regions of Saudi Arabia. Such scenarios can contribute in developing sustainable strategies for water resources management in water deficit regions.

Water resource management strategy for urban water purposes in Dili Municipality, Timor-Leste

ABSTRACT There are various factors in Dili’s water system responsible for the water scarcity problem in the city. Hence, there is a need for strategic plans which could be used as guidelines in improving its water system. The objective of this study was to formulate a strategic plan for water resource management in Dili. The process commenced with the identification of external and internal factors. The Analytical Hierarchy Process (AHP) was used to weight the score at the input stage. The alternative strategies were formulated with the Strengths, Weaknesses, Opportunities, and Threats (SWOT) matrix. Furthermore, the Quantitative Strategic Planning Matrix (QSPM) was used to select the priority strategy at the decision stage. Based on the results, a total of 13 internal and 13 external factors were identified, while five alternative strategies formulated. Moreover, the selected strategy as the priority for implementation has the capacity of developing the water resource management infrastructure in Dili, as well as improving the water supply system performance in meeting the urban water demand.

Towards quantification of the national water footprint in rice production of China: A first assessment from the perspectives of single-double rice

Rice is one of the most important crops in China, contributing to approximately 28% of total cereal yield. Despite substantial production, given that rice is a high water-consuming crop, the water shortage due to the irreversible decline in available water resources on a global scale induced by undergoing climate change will pose grave challenges to rice reproductive growth and related water resources utilization. As a consequence, investigating the responses of rice productivity and water consumption to more pronounced climate changes is of great significance for water resources sustainable utilization in terms of reducing irrigation water requirements and ensuring food security. Present water footprint (WF) methods do not calculate the weighted average of each WF component at the national level when evaluating the effects of prospective climate change upon rice production. The national water footprint (NWF), i.e. taking the share of each province in the total production of crops as weighting factors, has been regarded as an effective approach to determine where each WF component is originally located. In this study, the temporal change characteristics of NWF for single-rice (SR), early-rice (ER) and late-rice (LR) in different agro-ecological zones across China during 2001–2010 were assessed for the first time. The results exhibited that NWF of rice was an estimated 304,848 million cubic meters (MCM) per year. The SR accounted for the greatest portion of NWF, followed by ER and LR. The NWF rank was SR-V > SR-I > ER-VI > SR-IV > LR-III > LR-VI > SR-II > ER-III. The blue water footprint (WFb) presents decreasing trends in most agro-ecological zones (SR-I, SR-II, SR-IV, ER-III and LR-VI), while green water footprint (WFg) exhibits increasing trends within these regions. This study provides a beneficial approach for decision-making processes aiming at better agricultural water resources management strategies to alleviate water resources scarcity and reduce food risk in the context of surging demand, which will support agricultural water resources management of China towards a more balanced direction at the national level.

Determining of the Water Quality of the Ping River at Different Seasons in Northern Thailand

The water quality in a river (water environment) is very important for human health and aquatic organisms. In 2015, the highly regarded Water Resources Management Strategy of Thailand was announced by The Ministry of Industry in Thailand. In this study, the water quality of the Ping river in Northern Thailand, including Chiang Mai and Lamphun provinces, was focused on and measured for three different seasons (summer, rainy, and winter seasons). Anions (F-, Cl-, NO2-, NO3-, and SO42-) and cations (Na+, Mg2+, Si4+, S6+, K+, and Ca2+) were qualified by an ion chromatograph and an inductively coupled plasma optical emission spectrometry, respectively. The concentration of anions and cations (except for Mg2+ and Ca2+) in the Ping river at upstream (countryside) locations were lower than that at downstream (closer main city) locations, which indicated that the fertilizers, industrial or household wastewaters had been flowing into the Ping river at downstream locations. Additionally, the concentration of anions and cations in the rainy season was higher than other seasons. The present results provide the water quality of the Ping river which was not yet reported officially by the Thailand government.

Climatic seasonality and water quality in watersheds: a study case in Limoeiro River watershed in the western region of São Paulo State, Brazil.

Applying the climatological water balance (WB) concept to describe the relationship between climatic seasonality and surface water quality according to different forms of land use and land cover (LULC) is an important issue, but little explored in the literature. In this paper, we evaluate the influence of WB on surface water quality and its impacts when interacting with LULC. We monitored 11 sampling points during the four seasons of the year, from which we estimate WQI (water quality index) and TSI (trophic state index). We found an effect of the seasonality factor on both WQI values (F(3,30) = 12.472; p < 0.01) and in TSI values (F(3,30) = 6.967; p < 0.01). We noticed that LULC interferes in the way that the water balance influences the WQI and TSI values since in sampling points closest to higher urban density, with little or no riparian protection, the correlation between water balance and water quality was lower. In the stations that had the lowest water surplus and deficit, there was positive linearity between water balance and WQI. However, in the seasons when the surplus and water deficit recorded were extreme, there was no linearity. We conclude that water deficiency impairs the quality of surface water. In the extreme surplus water season, the homogeneity of WQI samples was lower, suggesting a higher interaction between rainwater and LULC. This study contributes to design management strategies of water resources, considering the climatic seasonality for optimization.

Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau

Understanding the spatiotemporal characteristics of water yield and its influencing factors is important for water resources management. In this study, we used the seasonal water yield model (SWYM) to assess the spatiotemporal water yield changes of the Lhasa River Basin from 1990 to 2015, and analyzed its influencing factors by focusing on precipitation, land cover, and normalized difference vegetation index (NDVI) change. We first examined the model through Morris screening sensitivity analysis and validated it with the observed flow data. Spatiotemporal variation of three indices of water yield, baseflow, quick flow, and local recharge were then assessed. Results showed that from 1990 to 2015, the baseflow, local recharge, and quick flow decreased by 67.03%, 80.21%, and 37.03%, respectively. The spatial pattern of water yield remained mostly unchanged. According to the contribution analysis, precipitation and NDVI change were the main factors affecting water yield in the Lhasa River Basin, while land cover change began to exert greater influence after 2010. A combination of climate change and human activities therefore drive water yield change, especially through vegetation change. Water resources management strategies should thus take into account the combination of rapidly changing climate and human activities.

Water Resources Management Strategies for Irrigated Agriculture in the Indus Basin of Pakistan

Agriculture of Pakistan relies on the Indus basin, which is facing severe water scarcity conditions. Poor irrigation practices and lack of policy reforms are major threats for water and food security of the country. In this research, alternative water-saving strategies are evaluated through a high spatio-temporal water footprint (WF) assessment (1997–2016) for the Punjab and Sindh provinces, which cover an irrigated area of 17 million hectares in the Indus basin of Pakistan. The SPARE:WATER model is used as a spatial decision support tool to calculate the WF and establish alternative management plans for more sustainable water use. The average water consumption (WFarea) is estimated to 182 km3 yr−1, composed of 75% blue water (irrigation water from surface water and groundwater sources), 17% green water (precipitation) and 8% grey water (water used to remove soil salinity or dilute saline irrigation water). Sugarcane, cotton, and rice are highly water-intensive crops, which consume 57% of the annual water use. However, WFarea can be reduced by up to 35% through optimized cropping patterns of the existing crops with the current irrigation settings and even by up to 50% through the combined implementation of optimal cropping patterns and improved irrigation technologies, i.e., sprinkler and drip irrigation. We recommend that the economic impact of these water-saving strategies should be investigated in future studies to inform stakeholders and policymakers to achieve a more sustainable water policy for Pakistan.

Improved Principal Component-Fuzzy Comprehensive Assessment Coupling Model for Urban River Water Quality: A Case Study in Chongqing, China

An improved principal component-fuzzy comprehensive assessment coupling model for urban river water quality is proposed, which fully considers the influence of water quality and quantity. This model can not only choose the key indexes, but also specify the spatial variation and class of water quality. This proposed model was used to assess the water quality of the Qingshui and Fenghuang streams in Chongqing, China. Data of twelve indexes used in the assessment were collected from 17 monitoring points. The assessment results show that the key indexes include TN, TP, NH3-N, CODcr, pH, DO and velocity. Water quality of 14 monitoring points is classified as class Bad V, and that of the remaining points is class V. Mainly affected by the deposition of garbage and discharge of domestic sewage, water quality of the midstream is the worst. The upstream is mainly influenced by farmland non-point source pollution and rural domestic sewage pollution. The downstream is close to the scenic area, and environmental control measures such as river dredging and artificial aeration are regularly carried out. The water quality of it is the best. The results provide valuable information that allow local environmental departments to discover the source of pollutant and formulate water resource management strategies.

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

Abstract. Assessing impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control, and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used for the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties into the simulated hydrologic variables. Here, a modeling framework was developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches, runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically based, infiltration excess), and simple-TOPMODEL (STP, physically based, saturation excess), were coupled with the Hillslope River Routing model to simulate surface/subsurface runoff and streamflow. A case study conducted in Santa Barbara County, California, reveals increased surface runoff in February and March but decreased runoff in other months, a delayed (3 d, median) and shortened (6 d, median) wet season, and increased daily discharge especially for the extremes (e.g., 100-year flood discharge, Q100). The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %. For projected changes in runoff and discharge, general circulation models (GCMs) and emission scenarios are two major uncertainty sources, accounting for about half of the total uncertainty. For the changes in seasonality, GCMs and hydrologic models are two major uncertainty contributors (∼35 %). In contrast, the contribution of hydrologic model parameters to the total uncertainty of changes in these hydrologic variables is relatively small (&lt;6 %), limiting the impacts of hydrologic model parameter equifinality in climate change impact analysis. This study provides useful information for practices associated with water resources, risk control, and ecosystem conservation and for studies related to hydrologic model evaluation and climate change impact analysis for the study region as well as other Mediterranean regions.

Drought analysis framework based on copula and Poisson process with nonstationarity

Droughts have been occurring with increased frequency and bringing with them considerable losses. Due to its nature, the best approach to monitoring and assessing droughts is in terms of stochastic theories. As a consequence, this study applied SPI in the hydrological drought detection, and examine/interpret drought-related phenomena. The 3-month SPI is used to decide the drought development and terminate phases based on the coincidence of hydrological drought and anomalies in precipitation.Four primary characteristics were determined to clarify the descriptions of drought magnitude and occurrence: duration, deficit, occurrence time and recurrence year. A stochastic process of drought is established to take account these characteristics using the copula function and a Poisson process as well as non-stationarity. Case study on the Shihmen and Zengwen reservoir watersheds in Taiwan revealed that hydrological drought events often coincide with meteorological drought, but some inconsistencies due to unusual hydrological hysteresis in rainfall and runoff. Our analysis raised several issues, including the choice of drought identification parameters and SPI time scale/ threshold, which should take into account anthropogenic activity and hydrological characteristics. A positive correlation between drought duration and drought deficit and a negative correlation between them and occurrence time were identified. It was also observed that subsequent drought event may be more severe due to the partial recovery of hydrological condition. In terms of non-stationarity, our results do not show strong evidence of long-term trend for drought characteristics. Some limitations could be due to the percentile-to-percentile basis of copula. This issue may need to be examined using non-stationary copula analysis or time-variant correlation-covariance models. In the end, we demonstrated that this framework can apply in forecasting drought conditions and generating synthetic droughts for use in the formulation of water resources management strategies and the development of drought preparedness plans.

Grid-scale agricultural land and water management: A remote-sensing-based multiobjective approach

This paper developed a remote-sensing-based multiobjective (RSM) approach to formulate sustainable agricultural land and water resources management strategies at a grid scale. To meet the spatial resolution and accuracy need of agricultural management, downscaled precipitation data sets were obtained with the help of global precipitation measurement (GPM) data and other spatial information. Spatial crop water requirement information were obtained via the combination use of the Penman-Monteith method, remote sensing information (MOD16/PET) and virtual water theory. Through integrating these spatial data and considering the impact of different spatial environments on crop growth, a grid-based integer multiobjective programming (GIMP) model was developed to determine best suitable crop planting types at all grids. GIMP can simultaneously consider several conflicting objectives: crop growth suitability, crop spatial water requirements, and ecosystem service value. Further, GIMP results were inputted into a grid-based nonlinear fractional multiobjective programming (GNFMP) model with three objectives: maximize economic benefits, maximize water productivity, and minimize blue water utilization, to optimize irrigation-water allocation. To verify the validity of the proposed approach, a real-world application in the middle reaches of Heihe River Basin, northwest China was conducted. Results show that the proposed method can improve the ecosystem service value by 0.36 × 108 CNY, the economic benefit by 21.85%, the irrigation-water productivity by 25.92%, and reduce blue water utilization rate by 24.32% comparing with status quo.

Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble

We analyze annual extremes of daily maximum and minimum surface air temperature and of daily rainfall in East Asia and the Korean peninsula. This study made intensive use of the simulation data available from the CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodels in historical and future experiments up to the year 2100, employing three different radiative forcings: RCP2.6, RCP4.5, and RCP8.5 (representative concentration pathways). Several reanalysis datasets are used to compare and evaluate the simulated climate extremes in the late twentieth century. We estimate the future changes in precipitation and temperature extremes in East Asia and Korea, and compare them to the global result, for the reference period 1986–2005. The rising rate of future cold extremes over East Asia and Korea is faster than that of warm extremes. This phenomenon appears more distinctly in Korea as a local scale, indicating more sensitivity of the Korean peninsula to global warming. The increase of the 20-year return level of maximum precipitation in the CMIP5 over East Asia by the end of twenty-first century is about 7% in the RCP2.6, 15% in the RCP4.5, and 35% in the RCP8.5 experiments, which exceed the corresponding global values. We also estimate the changes in precipitation extremes across East Asia as a function of the annual mean temperature variation at the same location. The CMIP5 sensitivity in maximum precipitation across East Asia is 5.5%/∘C, which is lower than the global figure (5.8%/∘C). The sensitivity for the Korean peninsula is 7.38%/∘C, indicating the strong impact of global warming to Korea. The results will be important in mitigating the detrimental effects of variations of climatic extremes and in improving the regional strategy for water resource and eco-environmental management, particularly for such areas in East Asia under significant changes in temperature and rainfall extremes.

Evaluating and Predicting the Effects of Land Use Changes on Hydrology in Wami River Basin, Tanzania

Understanding the variation in the hydrological response of a basin associated with land use changes is essential for developing management strategies for water resources. The impact of hydrological changes caused by expected land use changes may be severe for the Wami river system, given its role as a crucial area for water, providing food and livelihoods. The objective of this study is to examine the influence of land use changes on various elements of the hydrological processes of the basin. Hybrid classification, which includes unsupervised and supervised classification techniques, is used to process the images (2000 and 2016), while CA–Markov chain analysis is used to forecast and simulate the 2032 land use state. In the current study, a combined approach—including a Soil and Water Assessment Tool (SWAT) model and Partial Least Squares Regression (PLSR)—is used to explore the influences of individual land use classes on fluctuations in the hydrological components. From the study, it is evident that land use has changed across the basin since 2000 (which is expected to continue in 2032), as well as that the hydrological effects caused by land use changes were observed. It has been found that the major land use changes that affected hydrology components in the basin were expansion of cultivation land, built-up area and grassland, and decline in natural forests and woodland during the study period. These findings provide baseline information for decision-makers and stakeholders concerning land and water resources for better planning and management decisions in the basin resources’ use.

Pan evaporation modeling in different agroclimatic zones using functional link artificial neural network

Pan evaporation is an important climatic variable for developing efficient water resource management strategies. In the past, many machine learning models are reported in the literature for pan evaporation modeling using the different combinationof available climatic variables. In order to develop a novel model with improved accuracy and reduced computational complexity, the functional link artificial neural network (FLANN) is chosen as an architecture to estimate daily pan evaporation in three agro-climatic zones (ACZs) of Chhattisgarh state in east-central India. Single neuron and single layer in its structure make it less complex as compared to other multilayer neural networks and neuro-fuzzy based hybrid models. Estimation results obtained with the FLANN model are compared with those obtained by multi-layer artificial neural networks (MLANN) and two empirical methods using the same raw data and corresponding features. Statistical indices like root mean square error (RMSE), mean absolute error (MAE) and efficiency factor (EF) is also computed to evaluate the model performance. It is demonstrated that pan evaporation estimates obtained with the proposed FLANN models provide an improved estimation of pan evaporation (RMSE = 0.85 to 1.27 mmd-1, MAE = 0.63 to 0.95 mmd-1 and EF = 0.70 to 0.89) as compared to MLANN (RMSE = 0.94 to 1.58 mmd-1, MAE = 0.73 to 1.14 mmd-1 and EF = 0.62 to 0.88) and empirical (RMSE = 1.19 to 2.19 mmd-1, MAE = 0.91 to 1.62 mmd-1 and EF = 0.49 to 0.88) models in different ACZs.

Application of water-energy-food nexus approach for designating optimal agricultural management pattern at a watershed scale

The increasing demands of the population and the need for development obliged the optimal use and adaptive management of the watershed resources. Accordingly, it is necessary to adopt comprehensive measures to reach sustainable development goals. This objective can be achieved by the application of interdisciplinary and professional approaches through establishing dynamic and optimal balance in supply and demand resources. However, such important optimization approaches have been rarely practiced at the watershed scale. The present study has been therefore formulated to apply a linear water-energy-food nexus optimization for the Shazand watershed, Markazi Province, Iran. This approach was applied for planning 14 crops planted in orchard, irrigated farms, and rain-fed farms, between 2006 and 2014, and targeting water-energy-food nexus index (WEFNI) maximization. The connections among the water, energy, and food were then evaluated through determining the amount of consumption, mass productivity, and economic productivity of water and energy. The results of WEFNIs revealed that almond has the highest WEFNI with values of 0.92, 0.76, 0.76, 0.83, 0.86, 0.86, 0.87, 0.87, and 0.88. Whilst, potato with WEFNI of 0.05, 0.05, 0.05, 0.06, 0.09, 0.10 and 0.11, sugar cane with WEFNI of 0.10 and cucumber with WEFNI of 0.13 had the lowest scores and the corresponding lowest performance among the study crops. The outcomes of optimization study explained that the current situation of land use in the Shazand Watershed is unsuitable to minimize water and energy consumption and maximize benefit. The results can be used as an effective tool for designating proper soil and water resource management strategies in the region.