Improving agricultural production under water scarcity in Fars province, Iran

ABSTRACT Water scarcity is one of the major limiting factor for improving agricultural production in the world, which significantly affects agricultural production and livelihood of millions of people who live in arid and semi-arid regions. This case study presents the analysis of the effectiveness of Silica Moisture Absorbent Medium (SMAM, commercially available under the name Sanoplant), with regard to water saving and shortening the crop growth period. A cost-benefit analysis was carried out to assess the long term economic viability of SMAM. This case study integrates field measurements and observations on plant development, as well as the nitrogen content of the leafs and nitrogen availability in the soil. To assess the effectiveness of SMAM in saving water, enhancing plant growth and reducing mortality rate of crops, 15 scenarios (combinations of water amount and SMAM) were set for each of the three most widely cultivated crops in Iran: orange (Citrus sinensis), olive (Olea europea) and date (Phoenix dactylifera). The scenarios differed in the dosage of SMAM and different irrigation regimes, to find the optimal usage to increase water productivity in Fars agricultural regions, while maintaining a positive cost-benefit ratio. The results show that by using SMAM, the best results can be obtained by using 7 grams of SMAM per kilogram of soil and even decreasing irrigation by 50%

Balancing Water Resources Development and Environmental Sustainability in Africa: A Review of Recent Research Findings and Applications

Sustainable development in Africa is dependent on increasing use of the continent’s water resources without significantly degrading ecosystem services that are also fundamental to human wellbeing. This is particularly challenging in Africa because of high spatial and temporal variability in the availability of water resources and limited amounts of total water availability across expansive semiarid portions of the continent. The challenge is compounded by ambitious targets for increased water use and a rush of international funding to finance development activities. Balancing development with environmental sustainability requires (i) understanding the boundary conditions imposed by the continent’s climate and hydrology today and into the future, (ii) estimating the magnitude and spatial distribution of water use needed to meet development goals, and (iii) understanding the environmental water requirements of affected ecosystems, their current status and potential consequences of increased water use. This article reviews recent advancements in each of these topics and highlights innovative approaches and tools available to support sustainable development. While much remains to be learned, scientific understanding and technology should not be viewed as impediments to sustainable development on the continent.Civil Engineering and Geoscience

Investigating morphological responses to sediment flux alterations and land use changes in the Mara Wetland, Tanzania

The Mara River is the only perennial river of a vast semi-arid area, including the Mara Serengeti ecoregion in Kenya and Tanzania. The river sustains more than one million inhabitants and millions of wild animals. In its lower reaches, the Mara River forms a wide wetland before flowing into Lake Victoria. The wetland represents a rich ecosystem providing essential services, but it is being threaten by increasing human activities. Farming, grazing, fishing and deforestation to produce charcoal and open new crops have deeply modified the riparian vegetation spatial distribution and the habitat morphology. Additionally, the construction of a new dam is planned immediately upstream of the wetland for irrigation purposes and hydropower.This work is undertaken to set up a hydro-morphodynamic model to predict the short- and long-term effects of human activities on the Mara Wetland habitat. The model will be a tool to evaluate strategies to mitigate the negative effects of the activities.The Lower Mara River is poorly gauged and only a few scattered data and observations are available. Therefore, in October - November 2017 (dry period) and May 2018 (wet period) multidisciplinary field work was conducted along a 130 km stretch of the river. An unmanned aerial vehicle (UAV) was used to produce high resolution orthophoto mosaics and digital elevation models of selected areas. The UAV gave topography and ground observations on vegetation type, size and distribution, and other features of unattainable areas. A sonar was used to map the bathymetry of some stretches of river and wetland. River discharge was measured on 4 locations. Bed sediments and water samples were collected from 8 spots to analyse sediment granulometry and suspended sediment concentration. Results suggest that, at wetland inlet, the river is particularly rich in suspended sediment, with measured averaged concentrations of the order of 500 mg L-1 and peaks of 2700 mg L-1. The wetland, thanks to its extent and dense vegetation cover, traps the 90% of the suspended sediments and releases clear waters to the Lake Victoria. The future placement of the dam may have a strong influence: without an adequate management, the dam solid and liquid discharge regulation may further trigger morphological changes and jeopardize the wetland ecosystem

Suspended sediment load prediction of river systems: An artificial neural network approach

Information on suspended sediment load is crucial to water management and environmental protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio Grande) in USA are estimated using artificial neural network (ANN) modeling approach. A multilayer perceptron (MLP) ANN with an error back propagation algorithm, using historical daily and weekly hydroclimatological data (precipitation P(t), current discharge Q(t), antecedent discharge Q(t-1), and antecedent sediment load SL(t-1)), is used to predict the suspended sediment load SL(t) at the selected monitoring stations. Performance of ANN was evaluated using different combinations of input data sets, length of record for training, and temporal resolution (daily and weekly data). Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient (R), mean absolute percent error (MAPE) and model efficiency (E). Comparison of training period length was also made (4, 3 and 2 years of training and 1, 2 and 3 years of testing, respectively). The model efficiency (E) and R2 values were slightly higher for the 4 years of training and 1 year of testing (4 * 1) for Mississippi River, indifferent for Missouri and slightly lower for Rio Grande River. Daily simulations using Input 1 (P(t), Q(t), Q(t-1), SL(t-1)) and three years of training and two years of testing (3 * 2) performed better (R2 and E of 0.85 and 0.72, respectively) than the simulation with two years of training and three years of testing (2 * 3) (R2 and E of 0.64 and 0.46, respectively). ANN predicted daily values using Input 1 and 3 * 2 architecture for Missouri (R2 = 0.97) and Mississippi (R2 = 0.96) were better than those of Rio Grande (R2 = 0.65). Daily predictions were better compared to weekly predictions for all three rivers due to higher correlation within daily than weekly data. ANN predictions for most simulations were superior compared to predictions using MLR, MNLR and ARIMA. The modeling approach presented in this paper can be potentially used to reduce the frequency of costly operations for sediment measurement where hydrological data is readily available.Artificial neural network (ANN) Sediment prediction Multiple linear regressions (MLR) Multiple non-linear regression (MNLR) Autoregressive integrated moving average (ARIMA) Mississippi Missouri Rio Grande

A comment on Chinese policies to avoid negative impacts on river ecosystems by hydropower projects

The rapid economic development of river basins depends on the excessive use of water resources. China experienced a rapid development of hydropower projects in the last two decades and thus faces many ecological and environmental issues, especially in ecologically sensitive areas. Environmental flow is an important management tool that requires attention in the environmental impact assessment of hydropower projects. Environmental flows are of great significance for maintaining river structures and protecting the health of both aquatic ecosystems and human sustainable livelihoods. Although the government authorities have done much work in this area and attempted to consider technical requirements to address the negative externalities of hydropower projects, there are still defects in the basic procedures, calculation methods, and ultimately implementation process from policy to operationalization in terms of environmental flows. The official standards for environmental flows assessment mainly appear in two documents: 1. specification for calculation of environmental flow in rivers and lakes; and 2. code for calculation ecological flow of hydropower projects. This paper reviewed the overarching framework of the two documents and then summarized their fitness in terms of environmental flows implementation in hydropower projects. The research status of environmental flows and future directions for China were also proposed in this paper.Applied SciencesWater Resource

Analysing the elevation-distributed hydro-climatic regime of the snow covered and glacierised Hunza Basin in the upper Indus

In the high altitude Hindukush Karakoram Himalaya (HKH) mountains, the complex weather system, inaccessible terrain and sparse measurements make the elevation-distributed precipitation and temperature among the most significant unknowns. The elevation-distributed snow and glacier dynamics in the HKH region are also little known, leading to serious concerns about the current and future water availability and management. The Hunza Basin in the HKH region is a scarcely monitored, and snow- and glacier-dominated part of the Upper Indus Basin (UIB). The current study investigates the elevation-distributed hydrological regime in the Hunza Basin. The Distance Distribution Dynamics (DDD) model, with a degree day and an energy balance approach for simulating glacial melt, is forced with precipitation derived from two global datasets (ERA5-Land and JRA-55). The mean annual precipitation for 1997–2010 is estimated as 947 and 1,322 mm by ERA5-Land and JRA-55, respectively. The elevation-distributed precipitation estimates showed that the basin receives more precipitation at lower elevations. The daily river flow is well simulated, with KGE ranging between 0.84 and 0.88 and NSE between 0.80 and 0.82. The flow regime in the basin is dominated by glacier melt (45%–48%), followed by snowmelt (30%–34%) and rainfall (21%–23%). The simulated snow cover area (SCA) is in good agreement with the MODIS satellite-derived SCA. The elevation-distributed glacier melt simulation suggested that the glacial melt is highest at the lower elevations, with a maximum in the elevation 3,218–3,755 masl (14%–21% of total melt). The findings improve the understanding of the local hydrology by providing helpful information about the elevation-distributed meltwater contributions, water balance and hydro-climatic regimes. The simulation showed that the DDD model reproduces the hydrological processes satisfactorily for such a data-scarce basin.Water Resource