Verifying optimality of rainfed agriculture using a stochastic model for drought occurrence

It may be paradoxical but subsistence rainfed agriculture is the predominant source of food in Sub-Saharan Africa where the production uncertainty is associated with the stochastic nature of rainfall. This paper attempts to comprehend the rationale of this situation by a mathematical approach. Considering the level of drought severity as the zero-reverting Ornstein–Uhlenbeck process, optimality of rainfed agriculture is investigated in the context of stochastic control theory. Occurrence of drought terminating growth of crops is modelled with the concept of first exit time. A stochastic control problem allowing for virtual cost of irrigation, water stress to crops, and benefits of farming is formulated with irrigation effort as the control variable. The Hamilton–Jacobi–Bellman equation governing the optimal control is studied to identify the set of cost functions optimizing rainfed agriculture in an inverse problem approach. Data and information were collected in the coastal savanna agro-ecological zone of Ghana, to identify model parameters, formulate the stochastic control problem, solve the inverse problem, and then verify optimality of rainfed agriculture. The results indicated that rainfed agriculture is not optimal when the crop is more tolerant to water stress.     

Impact of spatial and temporal aggregation of input parameters on the assessment of irrigation scheme performance

The simulations of dynamic, spatially distributed non-linear models are impacted by the degree of spatial and temporal aggregation of their input parameters and variables. This paper deals with the impact of these aggregations on the assessment of irrigation scheme performance by simulating water use and crop yield. The analysis was carried out on a 7000 ha irrigation scheme located in Southern Spain. Four irrigation seasons differing in rainfall patterns were simulated (from 1996/1997 to 1999/2000) with the actual soil parameters and with hypothetical soil parameters representing wider ranges of soil variability. Three spatial aggregation levels were considered: (I) individual parcels (about 800), (II) command areas (83) and (III) the whole irrigation scheme. Equally, five temporal aggregation levels were defined: daily, weekly, monthly, quarterly and annually.

The results showed little impact of spatial aggregation in the predictions of irrigation requirements and of crop yield for the scheme. The impact of aggregation was greater in rainy years, for deep-rooted crops (sunflower) and in scenarios with heterogeneous soils. The highest impact on irrigation requirement estimations was in the scenario of most heterogeneous soil and in 1999/2000, a year with frequent rainfall during the irrigation season: difference of 7% between aggregation levels I and III was found. Equally, it was found that temporal aggregation had only significant impact on irrigation requirements predictions for time steps longer than 4 months. In general, simulated annual irrigation requirements decreased as the time step increased. The impact was greater in rainy years (specially with abundant and concentrated rain events) and in crops which cycles coincide in part with the rainy season (garlic, winter cereals and olive).

It is concluded that in this case, average, representative values for the main inputs of the model (crop, soil properties and sowing dates) can generate results within 1% of those obtained by providing spatially specific values for about 800 parcels.     

Regional impacts of climate change on irrigation water demands

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U₂), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U₂. Consequently, the reference evapotranspiration, modeled by the Penman–Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U₂). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd.     

Water management for irrigation,crop yield and social attitudes: a socio-agricultural agent-based model to explore a collective action problem

ABSTRACT

When rainfall does not meet crop water requirements, supplemental irrigation is needed to maintain productivity. On-farm ponds can prevent excessive groundwater exploitation – to the benefit of the whole community – but they reduce the cultivated area and require investments by each farmer. Thus, choosing the source of water for irrigation (groundwater vs on-farm pond) is a problem of collective action. An agent-based model is developed to simulate a smallholder farming system; the farmers’ long-/short-view orientation determines the choice of the water source. We identify the most beneficial water source for economic gain and its stability, and how it can change across communities and under future climate scenarios. By using on-farm ponds, long-view-oriented farmers provide collective advantages but have individual advantages only under extreme climates; a tragedy of the commons is always possible. Changes in farmers’ attitudes (and hence sources of water) based on previous experiences can worsen the economic outcome.     

Optimal intraseasonal irrigation water distribution

A dynamic programming model is presented to optimise the intraseasonal distribution of irrigation water to a single crop under the constraints of limited water available and predetermined irrigation timing. The system underlying the model is characterised by two discrete state variables: the available soil-water in the root zone and the net quantity of water to be transferred to the root zone of the crop. Transition equations from one state to another are used in response to irrigation decisions. A multiplicative yield function is employed for estimating the crop yield as it is influenced by soil moisture. A numerical example is presented to illustrate the proposed procedure.     

UNIQUE ASPECTS OF MODELING IRRIGATI0N-INDUCED SOIL EROSION

llNTRODUCTIONIrrigationisimp0rtanttof00dpr0ducti0nthr0ughoutthew0rld.Irrigati0nisused0naboutl5theworld'scropland(KendalIandPimentel,l994)and5%ofthew0rld'sfoodproductionland,whichincludesrangelandandpermanentcr0pland(FAO,l998).However,irrigatedlandproducesmorethan30%ofthew0rld'sf0od(Tribe,1994),whichis2l/2timesasmuchperunitareacomparedt0n0n-irrigatedproducti0n(KendallandPimentel,1994).IntheUnitedStates,approximatelyl5theharvestedcr0plandisirrigated,butalmost40thet0talcr0pvalue…     

Assessment of impact of climate change and adaptation strategies on maize production in Uganda

Globally, various climatic studies have estimated a reduction of crop yields due to changes in surface temperature and precipitation especially for the developing countries which is heavily dependent on agriculture and lacks resources to counter the negative effects of climate change. Uganda's economy and the wellbeing of its populace depend on rain-fed agriculture which is susceptible to climate change. This study quantified the impacts of climate change and variability in Uganda and how coping strategies can enhance crop production against climate change and/or variability.The study used statistical methods to establish various climate change and variability indicators across the country, and uses the FAO AquaCrop model to simulate yields under possible future climate scenarios with and without adaptation strategies. Maize, the most widely grown crop was used for the study. Meteorological, soil and crop data were collected for various districts representing the maize growing ecological zones in the country.Based on this study, it was found that temperatures have increased by up to 1 °C across much of Uganda since the 1970s, with rates of warming around 0.3 °C per decade across the country. High altitude, low rainfall regions experience the highest level of warming, with over 0.5 °C/decade recorded in Kasese. Rainfall is variable and does not follow a specific significant increasing or decreasing trend. For both future climate scenarios, Maize yields will reduce in excess of 4.7% for the fast warming-low rainfall climates but increase on average by 3.5% for slow warming-high rainfall regions, by 2050. Improved soil fertility can improve yields by over 50% while mulching and use of surface water management practices improve yields by single digit percentages. The use of fertilizer application needs to go hand in hand with other water management strategies since more yields as a result of the improved soil fertility leads to increased water stress, especially for the dry climates.     

Simulating sensitivity of soil moisture and evapotranspiration under heterogeneous soils and land uses

Soil moisture (SM) plays an important role in land surface and atmospheric interactions. It modifies energy balance at the surface and the rate of water cycling between the land and atmosphere. In this paper we provide a sensitivity assessment of SM and ET for heterogeneous soil physical properties and for three land uses including irrigated maize, rainfed maize, and grass at a climatological time-scale by using a water balance model. Not surprisingly, the study finds increased soil water content in the root zone throughout the year under irrigated farming. Soil water depletes to its lowest level under rainfed maize cultivation. We find a ‘land use’ effect as high as 36 percent of annual total evapotranspiration, under irrigated maize compared to rainfed maize and grass, respectively. Sensitivity analyses consisting of comparative simulations using the model show that soil characteristics, like water holding capacity, influence SM in the root zone and affect seasonal total ET estimates at the climatological time-scale. This ‘soils’ effect is smaller than the ‘land use’ effect associated with irrigation but, it is a source of consistent bias for both SM and ET estimates. The ‘climate’ effect basically masks the ‘soils’ effect under wet conditions. These results lead us to conclude that appropriate representation of land use, soils, and climate are necessary to accurately represent the water and energy balance in real landscapes.     

基于模拟优化与正交试验的库塘联合灌溉系统水资源调控

依托灌溉试验站田间降水-作物耗水-土壤水相互转化的长序列试验成果,构建灌区田间尺度水量蓄-耗-灌-排全过程的水资源模拟模块,结合系统仿真方法,建立库塘联合灌溉系统水量分配仿真模拟模型,以保障灌区基本需水(包括农村生活需水与生态环境需水)供水安全前提下的经济效益最大化为目标,运用正交试验选优原理,构建了库塘联合灌溉系统水资源优化调控模型,形成了基于仿真模拟与正交试验优化的库塘联合灌溉系统水资源优化调控技术体系,并应用于巢湖流域大官塘水库灌区,明确了灌区合理的工程布局规格与规模,确定了适宜的节水灌溉技术模式与灌溉制度,制定了塘坝和水库科学的调度规则,提出了具有可操作性的作物种植结构调整规则,提高了灌区径流拦蓄利用率,提升了塘坝和水库年际调蓄供水能力,增强了抗旱减灾能力,为巢湖流域水库灌区综合治理、库塘联合灌区水量分配方案、水库和塘坝调度规则及作物灌溉制度等地制定提供理论依据.     

Assessing crop yield and crop water productivity and optimizing irrigation scheduling of winter wheat and summer maize in the Haihe plain using SWAT model

Haihe plain is an important food production area in China, facing an increasing water shortage. The water used for agriculture accounts for about 70% of total water resources. Thus, it is critical to optimize the irrigation scheduling for saving water and increasing crop water productivity (CWP). This study first simulated crop yield and CWP for winter wheat and summer maize in historical scenario during 1961–2005 for Haihe plain using previously well‐established Soil and Water Assessment Tool model. Then, scenarios under historical irrigation (scenario 1) and sufficient irrigation (scenario 2) were, respectively, simulated both with sufficient fertilizer. The crop yield in scenario 2 was considered as the potential crop yield. The optimal irrigation scheduling with sufficient fertilizer (scenario 3) was explored by iteratively adjusting irrigation scheduling based on the scenario 1 and previous studies related to water stress on crop growth. Results showed that net irrigation amount was, respectively, reduced 23.1% and 18.8% in scenario 3 for winter wheat and summer maize when compared with scenario 1. The CWP was 12.1% and 8.2% higher with very slight change of crop yield. Using optimal irrigation scheduling could save 8.8 × 10⁸ m³ irrigation water and reduce about 16.3% groundwater over‐exploitation in winter wheat growth period. The corresponding yield was 18.5% and 12.9% less than potential yield for winter wheat and summer maize but using less irrigation water. Therefore, it could be considered that the optimal irrigation was reasonable, which provided beneficial suggestions for increasing efficiency of agricultural water use with sustainable crop yield in Haihe plain. Copyright © 2013 John Wiley & Sons, Ltd.     

Catchment scale hydrology of an irrigated cropping system under soil conservation practices

Soil erosion by water is a pressing environmental problem caused and suffered by agriculture in Mediterranean environments. Soil conservation practices can contribute to alleviating this problem. The aim of this study is to gain more profound knowledge of the effects of conservation practices on soil losses by linking crop management and soil status to runoff and sediment losses measured at the outlet of a catchment during seven years. The catchment has 27.42 ha and is located in a commercial farm in southern Spain, where a package of soil conservation practices is an essential component of the farming system. The catchment is devoted to irrigated annual crops with maize–cotton–wheat as the primary rotation. Mean annual rainfall‐induced runoff coefficient was 0.14 and mean annual soil loss was 2.4 Mg ha^?1 y^?1. Irrigation contributed to 40% of the crop water supply, but the amount of runoff and sediment yield that it generated was negligible. A Principal Components Analysis showed that total soil loss is determined by the magnitude of the event (rainfall and runoff depths, duration) and by factors related to the aggressiveness of the events (rainfall intensity and preceding soil moisture). A third component showed the importance of crop coverage to reduce sediment losses. Cover crops grown during autumn and early winter and crop residues protecting the soil surface enhanced soil conservation notably. The role of irrigation to facilitate growing cover crops in Mediterranean environments is discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Climate impacts on global irrigation requirements under 19 GCMs,simulated with a vegetation and hydrology model

Abstract

This study quantifies global changes in irrigation requirements for areas presently equipped for irrigation of major crop types, using climate projections from 19 GCMs up to the 2080s. Analysis is based on results from the global eco-hydrological model LPJmL that simulates the complex and dynamic interplay of direct and indirect climate change effects upon irrigation requirements. We find a decrease in global irrigation demand by ～17% in the ensemble median, due to a combination of beneficial CO₂ effects on plants, shorter growing periods and regional precipitation increases. In contrast, increases of >20% are projected with a high likelihood (i.e. in more than two thirds of the climate change scenarios) for some regions, including southern Europe, and, with a lower likelihood, for parts of Asia and North America as well. If CO₂ effects were not accounted for, however, global irrigation demand would hardly change, and increases would prevail in most regions except for southern Asia (where higher precipitation is projected). We stress that the CO₂ effects may not be realized everywhere, that irrigation requirements will probably increase further due to growing global food demand (not considered here), and that a significant amount of water to meet future irrigation requirements will have to be taken from fossil groundwater, environmental flow reserves or diverted rivers.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Konzmann, M., Gerten, D., and Heinke, J., 2013. Climate impacts on global irrigation requirements under 19 GCMs, simulated with a vegetation and hydrology model. Hydrological Sciences Journal, 58 (1), 1–18.     

Probabilistic estimation of irrigation requirement under climate uncertainty using dichotomous and marked renewal processes

This study addresses estimation of net irrigation requirement over a growing season under climate uncertainty. An ecohydrological model, building upon the stochastic differential equation of soil moisture dynamics, is employed as a basis to derive new analytical expressions for estimating seasonal net irrigation requirement probabilistically. Two distinct irrigation technologies are considered. For micro irrigation technology, probability density function of seasonal net irrigation depth (SNID) is derived assessing transient behavior of a stochastic process which is time integral of dichotomous Markov process. Probability mass function of SNID which is a discrete random variable for traditional irrigation technology is also presented using a marked renewal process with quasi-exponentially-distributed time intervals. Comparing the results obtained from the presented models with those resulted from a Monte Carlo approach verified the significance of the probabilistic expressions derived and assumptions made.     

IHMS—Integrated Hydrological Modelling System. Part 1. Hydrological processes and general structure

A newly Integrated Hydrological Modelling System (IHMS) has been developed to study the impact of changes in climate, land use and water management on groundwater and seawater intrusion (SWI) into coastal areas. The system represents the combination of three models, which can, if required, be run separately. It has been designed to assess the combined impact of climate, land use and groundwater abstraction changes on river, drainage and groundwater flows, groundwater levels and, where appropriate, SWI. The approach is interdisciplinary and reflects an integrated water management approach. The system comprises three packages: the Distributed Catchment Scale Model (DiCaSM), MODFLOW (96 and 2000) and SWI models. In addition to estimating all water balance components, DiCaSM, produces the recharge data that are used as input to the groundwater flow model of the US Geological Survey, MODFLOW. The latter subsequently generates the head distribution and groundwater flows that are used as input to the SWI model, SWI. Thus, any changes in land use, rainfall, water management, abstraction, etc. at the surface are first handled by DiCaSM, then by MODFLOW and finally by the SWI. The three models operate at different spatial and temporal scales and a facility (interface utilities between models) to aggregate/disaggregate input/output data to meet a desired spatial and temporal scale was developed allowing smooth and easy communication between the three models. As MODFLOW and SWI are published and in the public domain, this article focuses on DiCaSM, the newly developed unsaturated zone DiCaSM and equally important the interfacing utilities between the three models. DiCaSM simulates a number of hydrological processes: rainfall interception, evapotranspiration, surface runoff, infiltration, soil water movement in the root zone, plant water uptake, crop growth, stream flow and groundwater recharge. Input requirements include distributed data sets of rainfall, land use, soil types and digital terrain; climate data input can be either distributed or non‐distributed. The model produces distributed and time series output of all water balance components including potential evapotranspiration, actual evapotranspiration, rainfall interception, infiltration, plant water uptake, transpiration, soil water content, soil moisture (SM) deficit, groundwater recharge rate, stream flow and surface runoff. This article focuses on details of the hydrological processes and the various equations used in DiCaSM, as well as the nature of the interface to the MODFLOW and SWI models. Furthermore, the results of preliminary tests of DiCaSM are reported; these include tests related to the ability of the model to predict the SM content of surface and subsurface soil layers, as well as groundwater levels. The latter demonstrates how the groundwater recharge calculated from DiCaSM can be used as input into the groundwater model MODFLOW using aggregation and disaggregation algorithms (built into the interface utility). SWI has also been run successfully with hypothetical examples and was able to reproduce the results of some of the original examples of Bakker and Schaars ( 2005 ). In the subsequent articles, the results of applications to different catchments will be reported. Copyright © 2010 John Wiley & Sons, Ltd.     

Cotton canopy reflectance under variable solar zenith angles: Implications of use in evapotranspiration models

Evapotranspiration (ET) is an important parameter in hydrologic processes and modelling. In agricultural watersheds with competing uses of fresh water including irrigated agriculture, estimating crop evapotranspiration (ET_c) accurately is critical for improving irrigation system and basin water management. The use of remote sensing-based basal crop coefficients is becoming a common method for estimating crop evapotranspiration for multiple crops over large areas. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), based on reflectance in the red and near-infrared bands, are commonly used for this purpose. In this paper, we examine the effects of row crop orientation and soil background darkening due to shading and soil surface wetness on these two vegetation indices through modelling, coupled with a field experiment where canopy reflectance of a cotton crop at different solar zenith angles, was measured with a portable radiometer. The results show that the NDVI is significantly more affected than the SAVI by background shading and soil surface wetness, especially in north–south oriented rows at higher latitudes and could lead to a potential overestimation of crop evapotranspiration and irrigation water demand if used for basal crop coefficient estimation. Relationships between the analysed vegetation indices and canopy biophysical parameters such as crop height, fraction of cover and leaf area index also were developed for both indices.     

Collective action for water harvesting irrigation in the Lerma-Chapala Basin,Mexico

Water and watersheds are difficult to separate for management purposes. Providing irrigation as a supplement to rainfall for crop production requires considerable collective action at the watershed level to mobilize labor and other resources, as well as to make decisions and implement the distribution of benefits. Small-scale water harvesting irrigation systems in Mexico have endured for centuries. They now face considerable challenges with changes in the ejido property rights over land and water, the growing importance of alternative sources of livelihoods, and increasing scarcity and competition for water within the river basins.     

Investigating Impacts of Climate Change on Irrigation Water Demands and Its Resulting Consequences on Groundwater Using CMIP5 Models

In this study, the impacts of climate change on crop water requirements and irrigation water requirements on the regional cropping pattern were evaluated using two climate change scenarios and combinations of 20 GCM models. Different models including CROPWAT, MODFLOW, and statistical models were used to evaluate the climate change impacts. The results showed that in the future period (2017 to 2046) the temperature in all months of the year will increase at all stations. The average annual precipitation decline in Isfahan, Tiran, Flavarjan, and Lenj stations for RCP 4.5 and RCP 8.5 scenarios are 18.6 and 27.6%, 15.2 and 18%, 22.5 and 31.5%, and 10.5 and 12.1%, respectively. The average increase in the evapotranspiration for RCP 4.5 and RCP 8.5 scenarios are about 2.5 and 4.1%, respectively. The irrigation water demands increases considerably and for some crops, on average 18%. Among the existing crops in the cropping pattern, barley, cumin, onion, wheat, and forage crops are more sensitive and their water demand will increase significantly. Results indicate that climate change could have a significant impact on water resources consumption. By considering irrigation efficiency in the region, climate change impacts will result in about 35 to 50 million m³/year, over-extraction from the aquifer. This additional exploitation causes an extra drop of 0.4 to 0.8 m in groundwater table per year in the aquifer. Therefore, with regard to the critical condition of the aquifer, management and preventive measures to deal with climate change in the future is absolutely necessary.     

Evaluating the effect of non-uniform and deficient irrigation—Part I

A method for evaluating the effect of non-uniform and deficient irrigation is presented. The method is based on a deterministic mathematical model that evaluates the effect of the soil water fluctuation in the root zone during the irrigation season on the crop yield.The problem is viewed in conjunction with the management strategy of irrigation water application under the assumption that only shortage of water causes a reduction in yield. The parameters describing the deficit zone of the application pattern, the soil-crop-atmosphere system and the crop response are incorporated in the model. Crop yield predictions are made through the relative water use and a multiplicative and an additive yield functions.A numerical example is used to illustrate the use of the model in sprinkler irrigation practice. The results agree well with those derived from the mathematical model evaluating the irrigation regime and the yield on each square of the irrigated area separately.     

Run off-on-out method and models for soil infiltrability on hill-slope under rainfall conditions

Infiltration is the process of water penetrating into soil, generally referred to as the downward movement of water from the soil surface[1,2]. This process is af-fected by water supply and the soil infiltrability, de-termines the amounts of water entering into soil pro-file and the surface runoff. Infiltrability is defined as the infiltration flux of a unit area under atmospheric pressure and sufficient water supply. The actual infil-tration rate and/or the infiltrability is expressed in m/s …