Identifying and Evaluating a Suitable Index for Agricultural Drought Monitoring in the Texas High Plains

Drought is a complex and highly destructive natural phenomenon that affects portions of the United States almost every year, and severe water deficiencies can often become catastrophic for agricultural production. Evapotranspiration (ET) by crops is an important component in the agricultural water budget; thus, it is advantageous to include ET in agricultural drought monitoring. The main objectives of this study were to (1) conduct a literature review of drought indices with a focus to identify a simple but simultaneously adequate drought index for monitoring agricultural drought in a semiarid region and (2) using the identified drought index method, develop and evaluate time series of that drought index for the Texas High Plains. Based on the literature review, the Standardized Precipitation‐Evapotranspiration Index (SPEI) was found to satisfy identified constraints for assessing agricultural drought. However, the SPEI was revised by replacing reference ET with potential crop ET to better represent actual water demand. Data from the Texas High Plains Evapotranspiration network was used to calculate SPEIs for the major irrigated crops. Trends and magnitudes of crop‐specific, time‐series SPEIs followed crop water demand patterns for summer crops. Such an observation suggests that a modified SPEI is an appropriate index to monitor agricultural drought for summer crops, but it was found to not account for soil water stored during the summer fallow period for winter wheat.     

Soil moisture‐based drought monitoring at different time scales: a case study for the U.S. Great Plains

Short‐term agricultural drought and longer term hydrological drought have important ecological and socioeconomic impacts. Soil moisture monitoring networks have potential to assist in the quantification of drought conditions because soil moisture changes are mostly due to precipitation and evapotranspiration, the two dominant water balance components in most areas. In this study, the Palmer approach to calculating a drought index was combined with a soil water content‐based moisture anomaly calculation. A drought lag time parameter was introduced to quantify the time between the start of a moisture anomaly and the onset of drought. The methodology was applied to four shortgrass prairie sites along a North‐South transect in the U.S. Great Plains with an 18‐year soil moisture record. Short time lags led to high periodicity of the resulting drought index, appropriate for assessing short‐term drought conditions at the field scale (agricultural drought). Conversely, long time lags led to low periodicity of the drought index, being more indicative of long‐term drought conditions at the watershed or basin scale (hydrological drought). The influence of daily, weekly, and monthly time steps on the drought index was examined and found to be marginal. The drought index calculated with a short drought lag time showed evidence of being normally distributed. A longer data record is needed to assess the statistical distribution of the drought index for longer drought lag times.     

PREDICTIVE ASSESSMENT OF SEVERITY OF AGRICULTURAL DROUGHTS BASED ON AGRO-CLIMATIC FACTORS1

ABSTRACT: Past historical evidence indicates that droughts have had great impacts on human life. Drought (or scarcity of water) is assessed based on two key factors, namely, the estimated water demand, and the expected water supply. The formulation of these key factors for a region largely depends on the agro-climatic and economic conditions. Consideration of one such key factor is the relationship between the crop yield and water deficit in the assessment and prediction of agricultural droughts. The varying nature of this relationship from crop to crop adds to the complexity of agricultural drought analysis. To overcome this difficulty in analyzing agricultural droughts of a region, it is adequate to consider and place emphasis on a single crop (i.e., an index crop) grown homogeneously over the major area of the region. From one year to another year, the pattern of water requirement during the growing season of an index crop is rather stationary, and the water supply in arid and semi-arid area is mainly from seasonal random precipitation. In a region, grain yield of the index crop and, in turn, assessment of the severity of drought can reasonably be predicted as a function of the time of crop sowing and the distribution of rainfall, provided that temporal and spatial effects of other contributing factors (crop variety, soil fertility status, crop disease, pest control, cultivation practices etc.) on grain yield are considered to be uniformly distributed (i.e., stable). A predictive method of assessing agricultural droughts in an arid area of western India is presented. The major crop (Pearl Millet) of this region is grown from. July through September. The formulation of the proposed predictive method inherently implies that the grain yield of the main crop is a reliable indicator of agricultural drought. In the development of this predictive relationship (i.e., a regression type model) a number of potential yet simple variables affecting the grain yield in the region were investigated. The soil moisture index, although generally considered significant compared to the simple variables, has been found to account for insignificant variation in the grain yield. Results of our investigations suggest that it would be advisable to exclude the soil moisture index variable from the model. The proposed regression model can be used in the prediction of grain yield of the main crop several months ahead of crop harvesting operations and, in turn, the assessment of agricultural drought severity as mild, moderate, or severe. Such an assessment is expected to be helpful to planners for arranging appropriate measures to effectively combat agricultural drought situations.     

PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.     

THE FLEXIBLE TOLERANCE METHOD FOR ESTIMATING HYDROLOGIC PARAMETERS IN THE ROOT ZONE1

In this study, a constrained minimization method, the flexible tolerance method, was used to solve the optimization problems for determining hydrologic parameters in the root zone: water uptake rate, spatial root distribution, infiltration rate, and evaporation. Synthetic soil moisture data were first generated using the Richards' equation and its associated initial and boundary conditions, and these data were then used for the inverse analyses. The results of inverse simulation indicate the following. If the soil moisture data contain no noise, the rate of estimated water uptake and spatial root distribution parameters are equal to the true values without using constraints. If there is noise in the observed data, constraints must be used to improve the quality of the estimate results. In the estimation of rainfall infiltration and surface evaporation, interpolation methods should be used to reduce the number of unknowns. A fewer number of variables can improve the quality of inversely estimated parameters. Simultaneous estimation of spatial root distribution and water uptake rate or estimation of evaporation and water uptake rate is possible. The method was used to estimate the water uptake rate, spatial root distribution, infiltration rate, and evaporation using long‐term soil moisture data collected from Nebraska's Sand Hills.     

Using soil moisture and spatial yield patterns to identify subsurface flow pathways

Subsurface soil water dynamics can influence crop growth and the fate of surface-applied fertilizers and pesticides. Recently, a method was proposed using only ground-penetrating radar (GPR) and digital elevation maps (DEMs) to identify locations where subsurface water converged into discrete pathways. For this study, the GPR protocol for identifying horizontal subsurface flow pathways was extended to a 3.2-ha field, uncertainty is discussed, and soil moisture and yield patterns are presented as confirming evidence of the extent of the subsurface flow pathways. Observed soil water contents supported the existence of discrete preferential funnel flow processes occurring near the GPR-identified preferential flow pathways. Soil moisture also played a critical role in the formation of corn (Zea mays L.) grain yield patterns with yield spatial patterns being similar for mild and severe drought conditions. A buffer zone protocol was introduced that allowed the impact of subsurface flow pathways on corn grain yield to be quantified. Results indicate that when a GPR-identified subsurface clay layer was within 2 m of the soil surface, there was a beneficial impact on yield during a drought year. Furthermore, the buffer zone analysis demonstrated that corn grain yields decreased as the horizontal distance from the GPR-identified subsurface flow pathways increased during a drought year. Averaged real-time soil moisture contents at 0.1 m also decreased with increasing distance from the GPR-identified flow pathways. This research suggests that subsurface flow pathways exist and influence soil moisture and corn grain yield patterns.     

COMPARISON OF TWO PESTICIDE LEACHING INDICES1

ABSTRACT: Agriculture is the leading cause of regional‐scale non‐point source (NPS) pollution in the world today. Indices of pesticide leaching in the vadose zone are well suited for estimating the spatial accumulation and distribution of NPS pollutants in the near surface. In this study the Attenuation Factor (AF) and the Leaching index (Li) are used to assess the near‐surface leaching potential for 32 important agrochemicals for world average agricultural soil properties and recharge rates. The AF and Li indices both require the same input data and appear to work well for nonpolar chemicals. In the effort reported here the AF and Li indices produced similar results for the 32 agrochemicals. Pesticides with high and moderate leaching potential are identified. The AF estimates were more constant than the Li estimates for changes in the compliance depth and recharge rate. The AF index is simpler to use than the Li index and, therefore, is more likely to be employed in the future for screening/ranking agrochemicals relative to regional‐scale NPS ground water vulnerability.     

Analysis of irrigation systems using sustainability-related criteria

Sustainable agricultural development as a desired goal in irrigation management is a result of recent public awareness of the scarcity of water for food production. In order to incorporate sustainability-related criteria in the analysis of irrigation systems, the present study aims at introducing environmental indices that represent irrigation water conservation and satisfactory production and income for farmers under stress conditions. An experiment was conducted in Chania, Greece, during the irrigation periods of 1989 and 1990. The irrigation water delivered to 40 experimental plots and the relevant soil moisture content at the root zone were recorded. The data, collected in real time, were used for the calculation of the corresponding environmental indices. The variation of indices in time and space was high, and demonstrated that up to 13% of water was delivered to crops, 82% was yield loss, and 84% was economic return. The study indicated that environmental indices could be easily computed by means of routinely collected data, and could also be incorporated into decision-making approaches, such as compromise programming, in order to develop policies for irrigation water allocation.     

NITROGEN AND ORGANIC MATTER LOSSES IN NO‐TILL CORN CROPPING SYSTEMS1

ABSTRACT: Intensive cropping systems based on mechanical movement of soil have induced land degradation in most agricultural areas due to soil erosion and soil fertility losses. Thus, farmers have been increasing fertilization rates to maintain an economically competitive crop yield. This practice has resulted in water quality degradation and lake eutrophication in many agricultural watersheds. Research was conducted in the Patzcuaro watershed in central Mexico to develop appropriate technology that prevents nonpoint source pollution from fertilizers. Organic matter (OM) and nitrogen (N) losses in runoff and nitrate (NO₃‐N) percolation in Andisols with corn under conventional till (CT) and no‐till (NT) treatments using variable percentages of crop residue as soil cover were investigated for steep‐slope agriculture. USLE type runoff plots were used to collect water runoff, while suction tubes with porous caps at 30, 60, and 90 cm depth were used to sample soil water solutes for NO₃‐N analyses. Results indicated a significant reduction of N and OM losses in runoff as residue cover increased in the NT treatments. Inorganic N in runoff was 25 kg/ha for NT without residue cover (NT‐0) and 6 kg/ha for the NT with 100 percent residue cover (NT‐100). Organic matter losses in runoff were 157 and 24 kg/ha for the NT‐0 and NT‐100 treatments, respectively. Nitrate‐N percolation was evident in CT and NT with 100 percent residue cover (NT‐100). However, NT‐100 had higher NO₃‐N concentration at the root zone, suggesting the possibility of reducing fertilization rates with the use of NT treatments.     

AN IRRIGATION SCHEDULING MODEL WHICH INCORPORATES RAINFALL PREDICTIONS1

ABSTRACT In humid areas appreciable amounts of rainfall complicate irrigation scheduling. This rainfall tends to give supplemental water application a low priority. As a result irrigation may be delayed until there is not enough time to cover the crop area before some drought damage occurs. To improve the management of irrigation systems, a scheduling model has been developed. The model's water application decisions incorporate climatological records, soil-plant data, current pan evaporation and rainfall, the number of fields to be irrigated, and 5-day weather forecasts. The model updates the soil moisture conditions, predicts impending water depletion, and if supplemental water is needed both the field priority and amount to be applied is indicated for each of the next 5 days. Errors introduced through the use of forecasts and long-term pan evaporation records have been slight because of the tri-weekly updating. Also natural rains which restore the root zone to maximum water holding capacity prevent long-term bias.     

Planning Agricultural Water Resources System Associated With Fuzzy and Random Features1

Li, Y.P. and G.H. Huang, 2011. Planning Agricultural Water Resources System Associated With Fuzzy and Random Features. Journal of the American Water Resources Association (JAWRA) 47(4):841‐860. DOI: 10.1111/j.1752‐1688.2011.00558.x Abstract: More and more regions where demand outstrips water resources availability have suffered from chronic severe shortages. It is particularly aggravated for agricultural irrigation systems where more water is necessary to support the rapidly increasing population and speedily developing economy. In this study, a two‐stage fuzzy‐stochastic programming (TFSP) method is developed for planning agricultural water resources management system in more efficient and sustainable ways. The developed method can address uncertain parameters described as probability distributions and fuzzy sets. It can also be used for analyzing various policy scenarios that are associated with different levels of economic consequences since penalties are exercised with recourse actions against any infeasibility. The developed method is applied to agricultural water‐resources management planning of the Zhangweinan River Basin, China. Solutions under various α‐cut levels and fuzzy dominance indices can be generated by solving a series of deterministic submodels, which can help determine optimized crop‐target values that could hedge appropriately against future available water levels. The results are helpful for water resources managers in not only making decisions of crop irrigation but also gaining insight into the tradeoffs between economic objective and system‐failure risk.     

SATELLITE MICROWAVE OBSERVATIONS OF SOIL MOISTURE VARIATIONS1

ABSTRACT: Results from studies in the Illinois-Indiana and Texas-Oklahoma areas indicate that satellite microwave observations at the 1.55 cm wavelength are responsive to relative moisture variations in the near surface layer of the soil. Because significant vegetation cover absorbs the 1.55 cm microwave emission from the soil, the target area must be predominately bare soil or low density vegetation cover for meaningful measurements to result. The 25 km resolution of the satellite sensor limits application of the microwave techniques to large areas such as watersheds or agricultural districts rather than individual fields. In general, at 1.55 cm. there is an inverse relationship between microwave brightness temperature and changes in soil moisture levels (as indicated by antecedent rainfall) in agricultural regions before the planting of crops or during the early growing season when vegetation cover is sparse. Even early season observations should be of great value in deciding on the time and type of crop planting and for initial irrigation scheduling when the root zone is still in close proximity to the surface.     

A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

Linking river, floodplain, and vadose zone hydrology to improve restoration of a coastal river affected by saltwater intrusion

Floodplain forests provide unique ecological structure and function, which are often degraded or lost when watershed hydrology is modified. Restoration of damaged ecosystems requires an understanding of surface water, groundwater, and vadose (unsaturated) zone hydrology in the floodplain. Soil moisture and porewater salinity are of particular importance for seed germination and seedling survival in systems affected by saltwater intrusion but are difficult to monitor and often overlooked. This study contributes to the understanding of floodplain hydrology in one of the last bald cypress [Taxodium distichum (L.) Rich.] floodplain swamps in southeast Florida. We investigated soil moisture and porewater salinity dynamics in the floodplain of the Loxahatchee River, where reduced freshwater flow has led to saltwater intrusion and a transition to salt-tolerant, mangrove-dominated communities. Twenty-four dielectric probes measuring soil moisture and porewater salinity every 30 min were installed along two transects-one in an upstream, freshwater location and one in a downstream tidal area. Complemented by surface water, groundwater, and meteorological data, these unique 4-yr datasets quantified the spatial variability and temporal dynamics of vadose zone hydrology. Results showed that soil moisture can be closely predicted based on river stage and topographic elevation (overall Nash-Sutcliffe coefficient of efficiency = 0.83). Porewater salinity rarely exceeded tolerance thresholds (0.3125 S m(-1)) for bald cypress upstream but did so in some downstream areas. This provided an explanation for observed vegetation changes that both surface water and groundwater salinity failed to explain. The results offer a methodological and analytical framework for floodplain monitoring in locations where restoration success depends on vadose zone hydrology and provide relationships for evaluating proposed restoration and management scenarios for the Loxahatchee River.     

Building resilience to drought in desertification-prone savannas in Sub-Saharan Africa: The water perspective

Securing sustainable livelihood conditions and reducing the risk of outmigration in savanna ecosystems hosted in the tropical semiarid regions is of fundamental importance for the future of humanity in general. Although precipitation in tropical drylands, or savannas, is generally more significant than one might expect, these regions are subject to considerable rainfall variability which causes frequent periods of water deficiency. This paper addresses the twin problems of “drought and desertification” from a water perspective, focusing on the soil moisture (green water) and plant water uptake deficiencies. It makes a clear distinction between long‐term climate change, meteorological drought, and agricultural droughts and dry spells caused by rainfall variability and land degradation. It then formulates recommendations to better cope with and to build resilience to droughts and dry spells. Coping with desertification requires a new conceptual framework based on green‐blue water resources to identify hydrological opportunities in a sea of constraints. This paper proposes an integrated land/water approach to desertification where ecosystem management supports agricultural development to build social‐ecological resilience to droughts and dry spells. This approach is based on the premise that to combat desertification, focus should shift from reducing trends of land degradation in agricultural systems to water resource management in savannas and to landscape‐wide ecosystem management.     

Assessment of Water Availability and Scarcity Based on Hydrologic Components in an Irrigated Agricultural Watershed Using SWAT

This study assesses the water availability and the water scarcity based on the hydrologic behavior under different weather conditions and crop coverages in an irrigated agricultural area of Rincon Valley in New Mexico using the SWAT (Soil and Water Assessment Tool) model. Two spatial crop coverages included normal (2008) and dry (2011) years with 14 different crop sets for each year. The SWAT was applied to generate the five essential indicators (surface flow, evapotranspiration, soil water, groundwater recharge, and irrigation water) to evaluate the integrated water availability based on hydrologic response units (HRUs) along with the Arrey Canal to supply irrigation water in the crop areas. The water availability index scores (0–1 range with 1 being the most available and 0 the least available) of alfalfa, corn, cotton, and pecans were 0.21, 0.56, 0.91, and 0.20, respectively, in the normal year and 0.16, 0.78, 0.88, and 0.24, respectively, in the dry year. In the dry year, water scarcity values were high in mostly alfalfa areas, whereas cotton areas have mostly no stress with good water availability. The major water users of crops, ranked in order, were alfalfa, pecans, cotton, and corn. During the dry year, water availability showed to be balanced in terms of water supply and demand by controlling crop patterns from reducing alfalfa acreage by 12% and increasing cotton acreage by 13%.     

PULSED TRICKLING EFFECTS ON SOIL MOISTURE DISTRIBUTION1

ABSTRACT Laboratory experiments were conducted to study effects of trickle emitter discharge rate on the distribution of soil moisture in a silty-clay loam soil. Both pulsed and continuous irrigation treatments were studied. A simulation model was used to evaluate the results obtained in the laboratory. The agreement between the predicted and measured soil moisture distribution patterns was quite good. For both pulsed and continuous applications, increasing trickle discharge rate resulted in a decrease in the horizontal component and an increase in the vertical component of the wetted soil profile. Compared to the continuous treatments, pulsed applications resulted in significant reduction in water loss below the root zone. Pulsed applications rates can replace continuous small discharge rates to reduce irrigation water runoff problems on heavy soils and with restricted infiltration allow the use of larger emitter orifices to decrease potential clogging of the trickle system.     

Prediction of Crop Production using Drought indices at Different Time Scales and Climatic Factors to Manage Drought Risk1

Sadat Noori, S.M., A.M. Liaghat, and K. Ebrahimi, 2011. Prediction of Crop Production Using Drought Indices at Different Time Scales and Climatic Factors to Manage Drought Risk. Journal of the American Water Resources Association (JAWRA) 48(1): 1‐9. DOI: 10.1111/j.1752‐1688.2011.00586.x Abstract: Drought causes great damage to rainfed and irrigated farming. Therefore, prediction of crop production during the drought period is essential in order to manage drought risk. Thus, proceeding to agricultural drought risk management can be very useful. This study shows the results of early crop prediction using the combination of climate factors and drought indices at different time scales. The study region was Hamadan, a semiarid region in Iran. The methodology demonstrated here has allowed the prediction of production several months before harvest. Moreover, the predictive models constructed have explained 89% of the temporal variability of wheat production. This method could be very efficient for managing crop production. Moreover, having clear prediction, decision makers can plan better for overcoming drought impacts to reduce crop uncertainty for farmers in insurance companies.