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Inevitable evaporation and sedimentation will influence reservoir operational performance if the two fluxes have been ignored when sizing reservoir systems. While this is to be expected, very few studies have quantified this influence. This study has analysed the effects of evaporation and sedimentation on reservoirs operational performance. It used seven reservoirs in Yesilirmak Basin, Turkey as case studies. The Dams serve a variety of purposes including irrigation, domestic and industrial, hydropower generation and flood control. Performance with regard to meeting these needs was characterized using reliability (time-based and volume-based), resilience, vulnerability and sustainability. The results showed that while ignoring both the evaporation and sedimentation during planning does affect subsequent operational performance, the influence of evaporation was more significant. Possible reasons are provided for the outcome and ways by which both can be accommodated during planning analyses thus mitigating the operational effects are suggested.

The main aim of this study is to assess the water availability in the Huong River Basin, Central Vietnam under the impacts of climate and population changes. Regarding the climate change impacts assessment, two options were adopted to produce fine scale climate projections over the river basin. First, coarse scale projections of rainfall and temperature by the HadCM3 General Circulation Model (GCM) were downscaled statistically using the Statistical Downscaling Model (SDSM). The second approach used bias-corrected dynamically downscaled output of HadGEM3-RA Regional Climate Model (RCM). The HEC-HMS hydrologic model was used for simulating the rainfall-runoff response in association with climate forcing. Finally, water availability was evaluated using the Water Evaluation and Planning (WEAP) model by taking into account plausible population changes within the river basin. The results showed that the future temperature will rise by 0.2 to 3.5 °C and annual rainfall will increase by 1 to 8%. Water shortages in 2080s were non-existent if population projections were ignored, which is to be expected given the projected increase in rainfall. When projected population increases were considered, however, there were unmet urban water demands in districts that were previously self-sufficient. However, the shortages remain very small relative to the gross domestic demand that they give no cause for alarm. The big message here is that the hydrology of the Huong River Basin in Vietnam will cope with the most severe projected climate to meet its agricultural and domestic water supply obligations well into the future. • Temperature and rainfall are both projected to rise in the Huong River Basin. • Simulated water shortage was small for the projected climate. • Population changes accentuated water scarcity within the Basin.

Accurate and reliable forecasting of reservoir inflows is crucial for efficient reservoir operation to decide the quantity of the water to be released for various purposes. In this paper, an artificial neural network (ANN) model has been developed to forecast the weekly reservoir inflows along with its uncertainty, which was quantified through accounting the model’s input and parameter uncertainties. Further, to investigate how the effect of uncertainty is translated in the process of decision making, an integrated simulation–optimization framework that consists of (i) inflow forecasting model; (ii) reservoir operation model; and (iii) crop simulation model was developed to assess the impacts of uncertainty in forecasted inflow on the irrigation scheduling and total crop yield from the irrigation system. A genetic algorithm was used to derive the optimal reservoir releases for irrigation and the area of irrigation. The proposed modeling framework has been demonstrated through a case example, Chittar river basin, India. The upper, lower, and mean of forecasted inflow from the ANN model were used to arrive at the prediction interval of the depth of irrigation, total crop yield, and area of irrigation. From the analysis, the ANN model forecast error of ± 69% to the mean inflow was estimated. However, the error to mean value of simulation for total irrigation, total yield, and area of irrigation was ± 13.3%, ± 6.5%, and ± 4.6%, respectively. The optimizer mainly contributed to the reduction in the errors (i.e., maximizing the total production with the optimal water releases from the reservoir irrespective of inflow to the reservoir). The results from this study suggested that the information on the uncertainty quantification helps in better understanding the reliability of the systems and for effective decision making.

This study evaluated the performance of microalgae under static cultivation for primary settled municipal wastewater (PSW) treatment as a low energy treatment process. The availability of a suitable carbon substrate was determined to be the main limiting factor affecting the algal treatment performance. To overcome the material cost of applying commercial sources of organic carbon, we evaluated pot ale – a carbohydrate-rich by-product from the production of malt whiskey – as a carbon substrate to promote microalgae growth and the removal of nitrogen (NH3–N) and phosphate (PO4–P) in PSW. For this, the mixotrophic microalgal species Chlorella vulgaris was used in batch experiments of PSW enriched with pot ale. Characterisation of the wastewater in the microalgae treatments compared with the control treatments (WWC) and wastewater with pot ale (WWPA) highlighted that C. vulgaris was a key organism in the algal-bacterial consortium responsible in inorganic N and P removal. We also observed a high variability in the characteristics of PSW across independent batches enriched with pot ale, which resulted in variability in the N and P removal efficiency by the alga, from 99% to 58% at reducing NH3–N, and from 94% to 58% at reducing PO4–P. As an extension of these batch-wise operated treatments, we investigated removal of NH3–N and PO4–P under semi-continuous operation with pot ale enrichment and found this to be a viable system for potential further development. This work highlights the use of pot ale enrichment with microalgae as a promising application for enhancing the efficiency of inorganic nutrient removal from PSW.

This work assessed the effects of joint operation on the performance of multipurpose, multireservoir systems vis-a-vis standalone operation. The analysis used the multipurpose Pong and Bhak...

Holistic water management approaches are essential under future climate and socio-economic changes, especially while trying to achieve inter-disciplinary societal goals such as the Sustainable Development Goals (SDGs) of clean water, hunger eradication, clean energy and life on land. Assessing water resources within a water-food-energy-environment nexus approach enables the relationships between water-related sectors to be untangled while incorporating impacts of societal changes. We use a systems modelling approach to explore global change impacts on the nexus in the mid-21st century in a complexwestern Himalayanwater resource system in India, considering a range of climate change and alternative socio-economic development scenarios. Results show that future socio-economic changes will have a much stronger impact on the nexus compared to climate change. Hydropower generation and environmental protection represent the major opportunities and limitations for adaptation in the studied system and should, thereby, be the focus for actions and systemic transformations in pursue of the SDGs. The emergence of scenario-specific synergies and trade-offs between nexus component indicators demonstrates the benefits that water resource systems models canmake to designing better responses to the complex nexus challenges associated with future global change. (c) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license

In the research and policy environment, local knowledge (LK) is increasingly seen as an important component of building the resilience of communities and delivering sustainable disaster risk reduction (DRR) approaches tailored to local contexts. Many studies focus on documenting LK in different contexts; however, far less emphasis has been given to understanding how external stakeholders (i.e. government, NGOs, consultants) engage with and perceive the value of LK for DRR. Through an intepretivist epistemology and a case study research design, this paper sets out to fill in this gap by engaging with external stakeholders involved with community-based flood risk management in Malawi. It bases its findings on a thematic analysis of qualitative data collected through focus group discussions (n = 7) and key informant interviews (n = 69) conducted in 2016 and 2017. The findings show that although there is an appreciation of the importance of LK in rhetoric, its inclusion in DRR practice remains limited. The strong dichotomy between local and scientific knowledge persists and it has led to the further marginalisation of LK. The international policy and research push for LK in DRR is therefore not translated to realities on the ground. To the best of our knowledge, this presents one of the first studies of external stakeholders' attitudes of LK and how these influence its overall position in DRR. The paper calls for further development of knowledge co-production processes that will be based on giving equal weight, recognition and importance to LK.

This study investigated the impacts of climate and socio-economic changes on water resources in the Beas-Sutlej river basin, Himalaya. Multiple-model ensembles of five climate models forced with th...

The crop water stress index (CWSI) is a reliable indicator of water status in plants and has been utilized for stress monitoring, yield prediction, and irrigation scheduling. Despite this, ...

The conventional extended aeration (EA) method has been considered as an effective method of secondary wastewater treatment in various installations around the world. The membrane bioreactor (MBR) method has been promoted by membrane manufacturers over the past decade as the most efficient treatment method in the industry. Research has shown the higher quality of MBR effluent compared with that of EA, but thecomparison has seldom included the financial aspects. If the effluent quality from both methods is acceptable for a given reuse application, then a major consideration in adopting either method must be financial (i.e., the capital expenditure (CAPEX) and operating expenses (OPEX) involved in their deployment). In this study, simulation of both methods is conducted for domestic wastewater treatment plants using the biological and financial models of CAPDETWorksTM. The CAPEX and OPEX findings from the simulation were re-based to October 2019 usingglobal price indices. Hydraulic loads from 500 to 5,000 m3/d were considered for three influent strengths: weak, medium, and strong. It is found that the EA method is less costly in meeting the effluent-quality requirements of certain reuse applications, while the MBR is recommended for the extra strong-strength influent especially for units smaller than 500m3/d, due to both effluent quality and lifetime cost.

International audience; Climate change in the Indian Himalayan region is being manifested in the loss of glaciers and altered patterns of monsoon rainfall. Simultaneously, rapid population growth together with economic development are increasing sectoral water demands and changing land use patterns. This study investigated the impact of this complex interplay on water resources in the Beas-Sutlej water resources system. The GFDL-CM3 model was used to describe RCP8.5 future meteorological conditions throughout the 21st century. Population and land use changes were projected under the Shared Socioeconomic Pathway-1 (SSP1). The water evaluation and planning (WEAP) system was applied for assessing sectoral water demands. The results showed increasing runoff during the premonsoon and monsoon seasons due to increased glaciers melting and more rainfall, respectively. It also emerged that irrigation water demand decreased moderately in Punjab (8%–13%) and Haryana (1%–9%); however, the situation was reversed in Rajasthan where it increased by 14%. Adaptation strategies were proposed including increased water allocation to Rajasthan and converting lands to cultivating more staple crops in Punjab and Haryana.

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable, hence, it is recommended to be used for similar applications.

Improving the ecological status of water sources is a growing focus for many developed and developing nations, in particular with reducing nitrogen and phosphorus in wastewater effluent. In recent years, mixotrophic micro-algae have received increased interest in implementing them as part of wastewater treatment. This is based on their ability to utilise organic and inorganic carbon, as well as inorganic nitrogen (N) and phosphorous (P) in wastewater for their growth, with the desired results of a reduction in the concentration of these substances in the water. The aim of this review is to provide a critical account of micro-algae as an important step in wastewater treatment for enhancing the reduction of N, P and the chemical oxygen demand (COD) in wastewater, whilst utilising a fraction of the energy demand of conventional biological treatment systems. Here, we begin with an overview of the various steps in the treatment process, followed by a review of the cellular and metabolic mechanisms that micro-algae use to reduce N, P and COD of wastewater with identification of when the process may potentially be most effective. We also describe the various abiotic and biotic factors influencing micro-algae wastewater treatment, together with a review of bioreactor configuration and design. Furthermore, a detailed overview is provided of the current state-of-the-art in the use of micro-algae in wastewater treatment.

The treatment of dairy wastewater using traditional wastewater treatment plants such as the activated sludge is not feasible as it will require high energy rate to provide the required oxygen. This paper presents experimental results of using Vermi Bio-filtration to treat wastewater from dairy industry. Samples of wastewater were collected from Prasanthi Farms located in a small village of Kollam district, India. The sample characteristics of pH, Total Dissolved Solids, Total Suspended Solids, Chemical Oxygen Demand, Oil & Grease and Dissolved Oxygen were measured before and after vermi bio-filtration treatment. The characteristics were measured at different time intervals of 12 hours (12, 24, 36, 48, and 60 hours). The percentage changes of the above parameters after 60 hours are -18, 79, 75, 67, 69 and -31% respectively. the method is inexpensive, it can capture all organic materials, capital and working expenses are low, there is no foul scent, worm biomass would be used as nourishment for the cows, fish agro-business.

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability.

Water scarcity is the major constraint to social-economic development in arid and semiarid regions, where irrigation needs to be scheduled properly for the main crops. In this study, a simulation-optimization model for crop optimal irrigation scheduling under uncertainty was developed to maximize the net benefit. The model integrated a water-driven crop model (AquaCrop) with the optimization model, and incorporated the generation technique for the interval values of hydrological parameters (i.e., precipitation and evapotranspiration) and crop market prices to deal with uncertainties in these variables. The water price was assumed constant. The model was calibrated based on field experimental data obtained in 2014 and validated using 2015 data. The field experiments involved spring wheat (Yongliang No. 4) at Shiyang River Basin Experiment Station in Wuwei City, Gansu Province of Northwest China. The model was then used to generate the optimal irrigation schedules under various irrigation amounts, irrigation events, initial soil water storage and crop market price under uncertainty. Results indicated that the model is applicable for reflecting the complexities of simulation-optimization under uncertainties for spring wheat irrigation water scheduling. The optimization results indicated that the optimal irrigation amount range was [185, 322] mm with the corresponding optimal net benefit of [1.05, 2.77] ×104 Yuan/hm2 and yield of [7.4, 7.6] kg/hm2 for extreme wet conditions in the basin (defined as the combination of the 5% frequency precipitation with 95% frequency evapotranspiration). For extreme dry conditions, the optimal irrigation amount range was [442, 507] mm with the optimal net benefit of [0.85, 2.64] ×104 Yuan/hm2 and the corresponding yield of [6.6, 7.4] kg/hm2. Results also showed that four irrigation events under higher initial soil water storage were more likely to produce the higher net benefit and the optimal net benefit would increase as the crop market price increases, as expected. This work can be used to guide irrigation management for local farmers.

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro-meteorological hazards. Here, we investigate the ability of bias-corrected Weather Research and Forecasting model output at 5-km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low-intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early-morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias-correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias-corrected high-resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias-corrected regional climate model output is also necessary for hydro-meteorological risk assessments in Himalayan catchments. © 2019. The Authors.

When based on the zones of available water in storage, hedging has traditionally used a single hedged zone and a constant rationing ratio for constraining supply during droughts. Given the usual seasonality of reservoir inflows, it is also possible that hedging could feature multiple hedged zones and temporally varying rationing ratios but very few studies addressing this have been reported especially in relation to adaptation to projected climate change. This study developed and tested Genetic Algorithms (GA) optimised zone-based operating policies of various configurations using data for the Pong reservoir, Himachal Pradesh, India. The results show that hedging does lessen vulnerability, which dropped from ≥ 60 % without hedging to below 25 % with the single stage hedging. More complex hedging policies, e.g. two stage and/or temporally varying rationing ratios only produced marginal improvements in performance. All this shows that water hedging policies do not have to be overly complex to effectively offset reservoir vulnerability caused by water shortage resulting from e.g. projected climate change.

River systems provide a diverse range of ecosystem services, examples include: flood regulation (regulating), fish (provisioning), nutrient cycling (supporting) and recreation (cultural). Developing water resources through the construction of dams (hydropower or irrigation) can enhance the delivery of provisioning ecosystem services. However, these hydrologic alterations result in reductions in less tangible regulating, cultural and supporting ecosystem services. This study seeks to understand how multiple impoundments, abstractions and transfers within the upper Beas River Basin, Western Himalayas, India, are affecting the delivery of supporting ecosystem services. Whilst approaches for assessing supporting ecosystem services are under development, the immediate aim of this paper is to set out a framework for their quantification, using the macroinvertebrate index Lotic-Invertebrate Index for Flow Evaluation (LIFE). LIFE is a weighted measure of the flow velocity preferences of the macroinvertebrate community. Flow records from multiple gauging stations within the basin were used to investigate flow variability at seasonal, inter-annual and decadal time scales. The findings show that both mean monthly and seasonal cumulative flows have decreased over time in the Beas River Basin. A positive hydroecological relationship between LIFE and flow was also identified, indicative of macroinvertebrate response to seasonal changes in the flow regime. For example, high LIFE scores (7.7–9.3) in the winter and summer seasons indicate an abundance of macroinvertebrates with a preference for high flows; this represents a high potential for instream supporting ecosystem services delivery. However, further analysis is required to understand these hydroecological interactions in the study basin and the impact on instream supporting ecosystem services delivery.

Crop water stress index (CWSI) is a reliable, economic and non-destructive method of monitoring the onset of water stress for irrigation scheduling purposes. Its application, however, is limited due to the need of obtaining the baseline canopy temperatures. This study developed a self-organizing map (SOM) based model to predict the CWSI using microclimatic variables, namely air temperature, canopy temperature and relative humidity. The canopy temperature measurements were made from Indian mustard crop grown in a humid sub-tropical agro-climate during the 2017 and 2018 cropping seasons. Eight levels of irrigation treatments (I1 – I8) based on maximum allowable depletion of available soil water were considered in the study. The CWSI for treatments I2 – I7 was computed using the empirical approach based on the experimentally measured baseline canopy temperatures from treatments I1 and I8. The number of data points used was 1260 and 1350 for model training and testing, respectively. The developed SOM model was evaluated using the error indices Nash-Sutcliffe efficiency (NSE), bias error (BE), absolute error (AE), and coefficient of determination (R2). The SOM predicted CWSI presented a good agreement with the baseline computed CWSI values during model training (R2 = 0.98, NSE = 0.97, AE = 0.018, BE = 0.0004) and testing (R2 = 0.98, NSE = 0.98, AE = 0.018, BE = 0.002). Treatment specific analysis was conducted to evaluate the performance of SOM predicted CWSI for different irrigation levels. Results indicated that the presence of zero CWSI values in a significant proportion in the dataset impacted the model prediction performance at low CWSI (

Optimal allocation of irrigation water both spatially and temporally is a great challenge in arid and semi-arid regions with limited water available for irrigation. In this study, a simulation-based optimization model for spatiotemporal allocation of irrigation water in arid region was developed, which integrated a distributed farmland water balance model for simulating hydrological processes in farmland and an optimization model to maximize irrigation and precipitation use efficiency (IPUE). A typical arid region, the middle reach of the Heihe River basin in Northwest China with a total farmland area of 2624 km2, was selected as the study area. Field experiment data in 2012 and 2013 were used to calibrate and validate the simulation model. Then, the simulation-based optimization model was used to optimize the spatial and temporal allocation of irrigation water under various scenarios. Results indicate that the model is applicable for reflecting the complexities of water consumption and irrigation water allocation in the study area. The optimal irrigation water depths averaged for different farmland units are 326 mm, 327 mm, 471 mm, and 495 mm for spring wheat, barley, vegetable, and maize, respectively. After optimal allocation of irrigation water, IPUEs can exceed 0.8 for all climate conditions, and IPUE in the wet year was lower than those in other circumstances because of higher precipitation in the wet year. Higher IPUE can be achieved when the gross irrigation water are 1.72, 1.90 and 1.97 billion m3 for the representative wet, normal, and dry years, respectively. The results can be used to guide irrigation water management by providing the information on when and how much to irrigate for each crop in different farmland units.

Current flood risk strategies in Malawi are characterized by community-based flood risk management (CB-FRM), even though studies explicitly documenting evidence of successful CB-FRM remain limited. This paper investigates the realities and challenges of CB-FRM as seen through a lens of different stakeholders. In order to capture the experiences of CB-FRM, a predominantly qualitative research framework was developed. In 2016, 11 focus group discussions with stakeholder groups (local communities, local government and non-governmental organisations) were held. Additionally, informal discussions, field visits, a short survey and an extensive desk study were undertaken. The findings were analysed according to the major themes that emerged related to the realities and challenges of specific stakeholder groups. Although response and relief still remain prominent components of CB-FRM in Malawi, a number of mitigation and preparedness activities is observed. However, a lack of in-country resources, relief-oriented aid approaches and an ‘aid dependency’ syndrome represent obstacles. Different stakeholder groups share similar challenges in terms of financing, participation, decentralised governance and project management. Lack of project sustainability and localised ownership also emerged as major challenges. The identified challenges shed light on the frontiers and directions in which improvements are needed, thus offering a valuable contribution to the existing knowledgebase.

This study investigated the adaptive capacity of static and dynamic hedging operating policies to shore up the performance, i.e. reliability and vulnerability, in irrigation water supply of Pong reservoir in India, during climate change. The policies were developed using genetic algorithm optimisation and used to force reservoir simulations for different climate change perturbed inflow series, whence derive the performance. For static hedging, the hedging fraction remains constant throughout the year while for dynamic hedging, this fraction varies monthly or seasonally. Results showed that static hedging was effective at tempering the systems vulnerability from its high of ≥60% to lower than 25%, while maintaining an acceptable volume-based reliability. Further simulations with dynamic hedging provided only modest improvements in these two indices. The significance of this study is its demonstration of the effectiveness of hedging as a climate change adaptation measure by limiting water shortage impacts. It also demonstrates that simple static hedging can match more complex dynamic hedging policies.

People possess a creative set of strategies based on their local knowledge (LK) that allow them to stay in flood-prone areas. Stakeholders involved with local level flood risk management (FRM) often overlook and underutilise this LK. There is thus an increasing need for its identification, documentation and assessment. Based on qualitative research, this paper critically explores the notion of LK in Malawi. Data was collected through 15 focus group discussions, 36 interviews and field observation, and analysed using thematic analysis. Findings indicate that local communities have a complex knowledge system that cuts across different stages of the FRM cycle and forms a component of community resilience. LK is not homogenous within a community, and is highly dependent on the social and political contexts. Access to LK is not equally available to everyone, conditioned by the access to resources and underlying causes of vulnerability that are outside communities&rsquo, influence. There are also limits to LK, it is impacted by exogenous processes (e.g., environmental degradation, climate change) that are changing the nature of flooding at local levels, rendering LK, which is based on historical observations, less relevant. It is dynamic and informally triangulated with scientific knowledge brought about by development partners. This paper offers valuable insights for FRM stakeholders as to how to consider LK in their approaches.

Crop growth is influenced by the energy partition and water–heat transfer in the soil and canopy, while crop growth affects the land surface energy distribution and soil water-heat dynamics. In order to simulate the above processes and their interactions, a new model, named CropSPAC, was developed considering both the growth of winter wheat and the water–heat transfer in Soil-Plant-Atmosphere Continuum (SPAC). In CropSPAC, the crop module depicts the dynamic changes of leaf area index (LAI), crop height, and the root distribution and outputs them to the SPAC module, while the latter outputs soil moisture conditions for the crop module. CropSPAC was calibrated and validated by field experiment of winter wheat in Yongledian, Beijing, with five levels of irrigation treatments, namely W0 (0 mm), W1 (60 mm), W2 (110 mm), W3 (170 mm), and W4 (230 mm). Results show that CropSPAC could predict the soil water and temperature distribution, and winter wheat growth with acceptable accuracy. For example, for the 0–1 m soil water storage, the R2 for W0, W1, W2, W3, and W4 is 0.90, 0.88, 0.90, 0.91, and 0.79, and the root mean square error (RMSE) is 17.24 mm, 27.65 mm, 20.47 mm, 22.35 mm, and 12.88 mm, respectively. For soil temperature along the soil profile, the R2 ranges between 0.96 and 0.98, and the RMSE between 1.22 °C and 1.94 °C. For LAI, the R2 varied from 0.76 to 0.96, and the RMSE from 0.52 to 0.67. We further compared the simulation results by CropSPAC and its two detached modules, i.e., crop and the SPAC modules. Results demonstrate that the coupled model could better reflect the interactions between crop growth and soil moisture condition, more suitable to be used under deficit irrigation conditions.

Summary This study employed a Monte-Carlo simulation approach to characterise the uncertainties in climate change induced variations in storage requirements and performance (reliability (time- and volume-based), resilience, vulnerability and sustainability) of surface water reservoirs. Using a calibrated rainfall–runoff (R–R) model, the baseline runoff scenario was first simulated. The R–R inputs (rainfall and temperature) were then perturbed using plausible delta-changes to produce simulated climate change runoff scenarios. Stochastic models of the runoff were developed and used to generate ensembles of both the current and climate-change-perturbed future runoff scenarios. The resulting runoff ensembles were used to force simulation models of the behaviour of the reservoir to produce ‘populations’ of required reservoir storage capacity to meet demands, and the performance. Comparing these parameters between the current and the perturbed provided the population of climate change effects which was then analysed to determine the variability in the impacts. The methodology was applied to the Pong reservoir on the Beas River in northern India. The reservoir serves irrigation and hydropower needs and the hydrology of the catchment is highly influenced by Himalayan seasonal snow and glaciers, and Monsoon rainfall, both of which are predicted to change due to climate change. The results show that required reservoir capacity is highly variable with a coefficient of variation (CV) as high as 0.3 as the future climate becomes drier. Of the performance indices, the vulnerability recorded the highest variability (CV up to 0.5) while the volume-based reliability was the least variable. Such variabilities or uncertainties will, no doubt, complicate the development of climate change adaptation measures; however, knowledge of their sheer magnitudes as obtained in this study will help in the formulation of appropriate policy and technical interventions for sustaining and possibly enhancing water security for irrigation and other uses served by Pong reservoir.

AquaCrop and GIS was used to quantify water consumption and water use efficiency.Water consumption decreased from east to west, mainly varied from crop area/types.WUE of maize may increase under climate change scenario SRES B2.WUE of spring wheat were predicted to decrease in 2030 but increase in 2050. Heihe River Basin is located in the arid inland area of northwest China and is facing serious water shortage problems. Since irrigation is the largest water consumer in the middle reaches of the Basin, it is crucial to study the crop yields and water consumption in order to improve the agricultural water productivity and to support sustainable economic development in this region. Based on field experiments in 2012 on typical crops, AquaCrop model was calibrated for seed maize, field maize and spring wheat; the models were validated using monitored data in 2013. Then considering the spatial distribution of soil types, groundwater depth, agricultural management and cropping patterns, ArcGIS was applied for the pre/post processing of the AquaCrop to quantify the spatial distribution of water consumption and water use efficiency (WUE) in a typical irrigation district and the whole middle reaches. Results indicate that the AquaCrop model can reasonably simulate the canopy cover development, biomass accumulation and crops yield, as well as the evolution of soil moisture in this area. For example, the Nash-Sutcliffe efficiency index for seed maize canopy cover was at least 0.91 during calibration and 0.96 during validation. Spatial analysis of simulated water consumption showed that total water consumption decreased from east to west due to the nature of the crops and the area cultivated. WUE for all the crops was above unity, with the vegetables recording the highest in 2012 and 2013 of 2.74kgm-3 and 3.19kgm-3 respectively. The least WUE was recorded for spring wheat, i.e. 1.19kgm-3 and 1.67kgm-3 in 2012 and 2013 respectively. Further simulations under future possible climate change scenarios showed that WUE of seed maize and field maize might rise to some extent, while WUE for spring wheat might decrease by 0.39% in 2030 but increase by 14.63% in 2050 under climate change scenario SRES B2.

In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.

Field irrigation water management depends on interactions among crop yield, soil water/salt and groundwater/salt in arid irrigation area with shallow-saline groundwater. This paper presents a novel uncertainty simulation-optimization framework for irrigation water allocation and sustainable agricultural environment, which integrates simulation of physical processes of soil-groundwater water and salt balance into an uncertainty-based optimization model. The impacts of crop evapotranspiration, soil water and salt and groundwater levels are interactively involved in the simulation model. Uncertainties (economic and crop parameters, available water amount) presented as fuzzy boundary intervals and probability distribution functions are considered in the optimization model. This field irrigation water allocation framework emphasizes the role of field soil water and salt movement processes to decision-making of irrigation water allocation. Then, the proposed simulation-optimization framework was applied to a case study in the Hetao Irrigation District, an arid area of northwest China where soil salinity is a serious environmental problem induced by irrigation and shallow groundwater. Therefore, optimal irrigation water allocation solutions can be generated for providing decision makers with reliable decision options where the maximum system benefits resulting from sustainable agricultural production are desired. Furthermore, the results can support analysis of interrelationships among system benefits, water allocation planning and groundwater depth, soil salt content constraints. Scenario analysis (groundwater table depth (GTD) = 1, 1.5, 2, 2.5, 3 m and no groundwater exchange consideration) showed that the maximum net benefit could be [27469, 44818] Yuan with the groundwater table depth of 1.5 m. Also, the irrigation water allocation changed when the salt constraint was considered, which indicates that the results obtained by the developed framework can alleviate soil salinization to a certain degree. Therefore, this framework can provide more effective information for the irrigation water management and soil salinization control, which is meaningful for the sustainable development of irrigation agriculture.

This paper investigated the long-term trends in precipitation from 16 stations located in the lower Shire catchment in Malawi over the period 1953–2010. Annual trend analysis was first considered, and in order to take into account seasonality and serial correlation, the different months of the year are considered. Trend significance was determined using the nonparametric Mann-Kendall (MK) test statistic while the determination of the trends magnitudes was achieved using Sen’s slope method. The homogeneity of trends was examined using the Van Belle and Hughes method. The results indicate that annual precipitation has increased, whereas, monthly precipitation revealed an upward trend in wet seasons (November to April) and a downward trend in dry seasons (May to October). The monthly peak trend analysis has shown upward trend in rainy months at all stations.

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.