Assessing size shifts amidst a warming climate in lakes recharged by the Asian Water Tower through satellite imagery.

Recent studies indicate that the Asian Water Tower (AWT) is at risk due to climate change, which can negatively impact water and food security in Asia. However, there is a lack of comprehensive information on lakes' spatial and temporal changes in this region. This information is crucial for understanding the risk magnitude and designing strategies. To fill this research gap, we analyzed 89,480 Landsat images from 1977 ± 2 to 2020 ± 2 to investigate the changes in the size of lakes recharged by the AWT. Our findings showed that out of the 209 lakes larger than 50 km2, 176 (84 %) grew during the wet season and 167 (81 %) during the dry season. 74 % of expanded lakes are located in the Inner Tibetan Plateau (TP) and Tarim basins. The lakes that shrank are found mainly in the Helmand, Indus, and Yangtze basins. Over the entire period, the area of shrinkage (55,077.028 km2 in wet season, 53,986.796 km2 in dry) markedly exceeded expansion (13,000.267 km2 in wet, 11,038.805 km2 in dry), with the drastic decline of the Aral Sea being a major contributor to shrinkage, accounting for 90 % of the total loss. From 1990 ± 2 to 2020 ± 2, alpine lakes mostly expanded, plain lakes mostly shrank, with the opposite trend from 1977 ± 2 to 1990 ± 2. Glacial loss and permafrost thawing under global warming in the Inner TP, Tarim Interior, Syr Darya, and Mekong basins were strongly correlated with lake expansion. However, permafrost discontinuities may prevent significant growth of lakes in the Indus and Ganges basins despite increased recharge. Our findings point to the prominence of the risk the lakes recharged by AWT face. Taking immediate action to manage these risks and adaptation is crucial as the AWT retreats and lake recharges are slowed.

Plant Physiological Analysis to Overcome Limitations to Plant Phenotyping.

Plant physiological status is the interaction between the plant genome and the prevailing growth conditions. Accurate characterization of plant physiology is, therefore, fundamental to effective plant phenotyping studies; particularly those focused on identifying traits associated with improved yield, lower input requirements, and climate resilience. Here, we outline the approaches used to assess plant physiology and how these techniques of direct empirical observations of processes such as photosynthetic CO2 assimilation, stomatal conductance, photosystem II electron transport, or the effectiveness of protective energy dissipation mechanisms are unsuited to high-throughput phenotyping applications. Novel optical sensors, remote/proximal sensing (multi- and hyperspectral reflectance, infrared thermography, sun-induced fluorescence), LiDAR, and automated analyses of below-ground development offer the possibility to infer plant physiological status and growth. However, there are limitations to such 'indirect' approaches to gauging plant physiology. These methodologies that are appropriate for the rapid high temporal screening of a number of crop varieties over a wide spatial scale do still require 'calibration' or 'validation' with direct empirical measurement of plant physiological status. The use of deep-learning and artificial intelligence approaches may enable the effective synthesis of large multivariate datasets to more accurately quantify physiological characters rapidly in high numbers of replicate plants. Advances in automated data collection and subsequent data processing represent an opportunity for plant phenotyping efforts to fully integrate fundamental physiological data into vital efforts to ensure food and agro-economic sustainability.

Are there universal soil responses to cover cropping? A systematic review.

Cover cropping is commonly acknowledged to promote soil health in agriculture. However, contradictory findings on the benefits of cover crops for soil health, crop productivity, economic and ecological factors, as well as the influence of inherent soil parameters on such benefits exist in the scientific literature. Here, we critically assessed evidence of cover crop benefits through a systematic review of the published literature. To access relevant papers, we searched the literature for cover crops and soil health indicators using Scopus (1996-2020), ScienceDirect (1996-2020) and Google scholar (1970-1996) with specific keywords and combinations. Only English research papers including experimental plots and control groups were considered. We analyzed 102 unique peer-reviewed papers and 1494 corresponding unique plots encompassing various cover crops, soil textures, climates, management systems and experimental duration (1-3 years, 4-6 years, 7-10 years and over 10 years). Strong evidence suggests that cover crops can enhance soil structure and promote soil health by improving soil physical and chemical properties, including saturated hydraulic conductivity (mean net change of 105.6 %), total organic carbon (10.1 %), and total nitrogen (20.2 %). On the other hand, cover crops exhibit weak effects on properties like bulk density and microporosity with fairly low values of net change. In most cases, cover crops increase the soil carbon content, including microbial biomass carbon (19.5 %) and particulate organic carbon (49.5 %). In this systematic review, we found limited studies on the effect of cover crops on soil health as influenced by soil texture, regional climate, rainfall and duration of the cover crop practices. The paucity of long-term regional systematic research of soil physics, chemistry and biology makes it difficult to forecast future implications of cover crops on soil health indicators.

Meteorological driving forces of reference evapotranspiration and their trends in California.

Reference evapotranspiration (ETo) is a variable that helps determine atmospheric pressure on living (reference) grass to release water into the atmosphere. For this purpose, four main driving forces: air temperature, air humidity, solar radiation, and wind speed need to be measured over the well-watered reference grass. The relative influence of these driving forces is region and climate-specific, with daily and seasonal variations. A clear understanding of the dynamic interactions of ETo's driving factors can illuminate the water and energy cycles of the earth and assist modelers with more accurate predictions of ETo. In this study, Pearson correlation, mutual information, and random forest feature importance analyses have been used to evaluate the relative importance of meteorological driving forces of ETo in California. To better understand the interrelations of these variables, 1,365,823 daily data samples from 237 standardized weather stations for 36 years have been clustered into homogeneous climatic zones and analyzed. To compensate for the effects of seasonality, feature importance analysis is also conducted on seasonal and monthly clustered data. Moreover, seasonal and annual trends of ETo and its driving factors are investigated for California and homogeneous zones using the Mann-Kendall test. Our findings reveal that for annually clustered data, solar radiation is the most influential driving factor of ETo in California. However, analysis of seasonal and monthly clustered data shows that vapor pressure deficit is the most informative factor during the summer and spring, while solar radiation is more important during the colder seasons. Results of trend analysis don't suggest a consistent monotonic trend for ETo and other variables for different seasons and zones. However, it is shown that agricultural regions with heavy irrigation dependence like the Central Valley are getting warmer and drier, especially during the irrigation season. This can adversely affect the water resources, agriculture industry, and food production of California, and modeling efforts like this can be very informative for future water resources management.

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation.

The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models' performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

Development and Application of a Predictive Model for Treated Wastewater Irrigation Management in a Semiarid Area.

The use of treated wastewater (TWW) as an alternative resource to fresh water (FW) for irrigation purposes is becoming increasingly important, especially in semiarid and arid regions. However, achieving success in crop production largely depends on the adoption of appropriate on-farm management strategies aimed at optimizing crop yields, maintaining soil productivity and safeguarding the environment. For this purpose, predictive models are of particular interest. A safe irrigation management (SIM) model developed and tested in this research was used to schedule irrigation under controlled management tailored to the use of 1) TWW and 2) FW and to reproduce farmers' strategies. These management strategies aim to improve actual irrigation practices, accounting for water quality, soil characteristics, and crop yield. The results of the application of SIM on a citrus farm in Souss-Massa, Morocco, show that the management strategy adopted by farmers withdraws considerable amounts of water and results in substantial drainage volumes compared to those in the SIM strategy. In the specific case of TWW, the strategy simulated by the SIM model resulted in a decrease in yield of approximately 4%, compared to the 23% decrease derived from the farmers' traditional strategy. Moreover, SIM allowed for great savings in terms of fertilizing elements and for the reduction in the movement of water and salts beyond the root zone, usually considered the main source of groundwater contamination. These results confirm the appropriateness of using prediction models and the accuracy of the SIM model in adapting irrigation strategies to TWW, which will be an integral part of the strategies that encourage their use in irrigated agriculture. Integr Environ Assess Manag 2020;16:910-919. © 2020 SETAC.

Optimize the spatial distribution of crop water consumption based on a cellular automata model: A case study of the middle Heihe River basin, China.

Globally, agriculture is by far the largest water consuming sector and in areas where water is scarce, the spatial optimization of crop water consumption used to improve irrigation benefits becomes critical for regional water management. The spatial heterogeneity of environmental parameters brings great challenge to spatial optimization. Therefore, cellular automaton (CA), crop suitability (CS), spatial distributed crop water consumption model and optimization model were integrated and applied on the middle reaches of Heihe River basin, northwest of China. The cellular automata based Water Consumption Optimization (CA-WCSO) model is not only a spatial dynamic optimization model for crop water consumption, but also a decision support tool that reflects the interaction between water consumption at field level and management regulations at regional level. Six optimization paths: i) forward progressive (FP), ii) forward interlacing (F-IL), iii) forward interpolation (F-IP), iv) reverse progressive (R-P), v) reverse interlacing (R-IL) and vi) reverse interpolation (R-IP) of crop water consumption for the baseline year and the planning year were applied on the study site. Results for baseline year (2015) demonstrate that the six optimization paths can slightly reduce the water consumption (>1.4%) but significantly improve the irrigation benefits of the region by 20.56%. Using CA-WCSO model, decision makers can modify model's constraints and select appropriate optimization path to get the optimized crop planting patterns and make future regional water allocation plans.

Operational resilience of reservoirs to climate change, agricultural demand, and tourism: A case study from Sardinia.

Many (semi-) arid locations globally, and particularly islands, rely heavily on reservoirs for water supply. Some reservoirs are particularly vulnerable to climate and development changes (e.g. population change, tourist growth, hydropower demands). Irregularities and uncertainties in the fluvial regime associated with climate change and the continuous increase in water demand by different sectors will add new challenges to the management and to the resilience of these reservoirs. The resilience of vulnerable reservoirs must be studied in detail to prepare for and mitigate potential impacts of these changes. In this paper, a reservoir balance model is developed and presented for the Pedra e' Othoni reservoir in Sardinia, Italy, to assess resilience to climate and development changes. The model was first calibrated and validated, then forced with extensive ensemble climate data for representative concentration pathways (RCPs) 4.5 and 8.5, agricultural data, and with four socio-economic development scenarios. Future projections show a reduction in annual reservoir inflow and an increase in demand, mainly in the agricultural sector. Under no scenario is reservoir resilience significantly affected, the reservoir always achieves refill. However, this occurs at the partial expenses of hydropower production with implications for the production of renewable energy. There is also the possibility of conflict between the agricultural sector and hydropower sector for diminishing water supply. Pedra e' Othoni reservoir shows good resilience to future change mostly because of the disproportionately large basin feeding it. However this is not the case of other Sardinian reservoirs and hence a detailed resilience assessment of all reservoirs is needed, where development plans should carefully account for the trade-offs and potential conflicts among sectors. For Sardinia, the option of physical connection between reservoirs is available, as are alternative water supply measures. Those reservoirs at risk to future change should be identified, and mitigating measures investigated.

More 'crop per drop': constraints and opportunities for precision irrigation in European agriculture.

Dwindling water supplies, increasing drought frequency and uncertainties associated with a changing climate mean Europe's irrigated agriculture sector needs to improve water efficiency and produce more 'crop per drop'. This paper summarizes the drivers for change, and the constraints and opportunities for improving agricultural water management through uptake of precision irrigation technologies. A multi-disciplinary and integrated approach involving irrigation engineers, soil scientists, agronomists and plant physiologists will be needed if the potential for precision irrigation within the field crop sector is to be realized.