Calibration of soil roughness and vegetation parameters in the SMOS retrieval algorithm and validation at local and global scale

La humedad del suelo y SMOS La humedad del suelo es un elemento clave que nos permite conocer los flujos de agua y energía entre el suelo y la atmósfera. Es además un parámetro de interés en aplicaciones hidrológicas y agricultura (Brocca et al., 2010), meteorología (de Rosnay et al., 2013), agricultura y predicción de riesgos naturales. La humedad del suelo en superficie se define como la fracción de agua contenida en un volumen de suelo húmedo, considerando una capa superficial de suelo de unos pocos centímetros (WMO, https://www.wmo-sat.info/oscar/variables/view/149). Puede expresarse de forma gravimétrica o de forma volumétrica. En este estudio se utiliza la relación entre el volumen de agua y el volumen de suelo que la contiene (m3·m-3). Dependiendo de su composición, todo suelo absorbe una cierta cantidad de agua hasta llegar a su punto de saturación. Existe por tanto una relación directa entre la humedad del suelo y su capacidad de infiltración, así como los flujos de calor sensible y humedad de la atmósfera, variables con una gran influencia en los modelos atmosféricos. La humedad del suelo es habitualmente una variable de iniciación de los modelos numéricos de predicción del tiempo (NWP) que permite mejorar su fiabilidad. Una aplicación significativa de la humedad del suelo a escala global es la monitorización de sequías y déficit hídrico en las plantas. El crecimiento y buen estado de la vegetación se relaciona con la cantidad de agua disponible en las raíces de la planta (hasta 1-2 m de profundidad), y esta a su vez, con la humedad superficial del suelo. La productividad de una planta dependerá por tanto de su nivel de estrés hídrico, humedad del suelo y el riesgo de hielo. La medida de la humedad del suelo desde satélite es posible gracias a la sensibilidad de la temperatura de brillo emitida en banda L a la humedad presente en la capa más superficial del suelo (~ 0-3 cm) (Escorihuela et al., 2010; Njoku and Kong, 1977). Esta relación se debe a que la emisividad del suelo en microondas está relacionada con su constante dieléctrica, y esta a su vez con la humedad del suelo. El satélite SMOS (Soil Moisture Ocean Salinity) forma parte de la primera misión cuyo objetivo es la estimación de la humedad del suelo (Kerr et al., 2012) y salinidad del agua en la Tierra (Reul et al., 2014). Su lanzamiento se produjo en Noviembre de 2009 por parte de la Agencia Espacial Europea (ESA) y fue seguido por el lanzamiento en Enero de 2015 de la misión SMAP (Soil Moisture Active Passive) por parte de NASA (Administración Nacional de la Aeronáutica y del Espacio) (Entekhabi et al., 2010), cuyo objetivo principal es la estimación de la humedad del suelo a escala global. La misión SMOS fue un proyecto ideado por la ESA en colaboración con el CDTI (Centro para el Desarrollo Tecnológico Industrial) en España, y el CNES (Centre National d’Études Spatiales) en Francia. El satélite SMOS posee un radiómetro interferométrico en banda L (1400 - 1427 MHz) de doble polarización (Kerr et al., 2001) con una resolución espacial de aproximadamente 43 km. Este radiómetro proporciona medidas multi-angulares y en polarización completa de temperatura de brillo de la Tierra con un periodo de revisita de 3 días. SMOS proporciona no solo medidas de humedad de suelo, sino también de espesor óptico de la vegetación. Este último parámetro se relaciona con ciertas características tales como el contenido en agua de la vegetación o la estructura de la misma (Grant et al., 2016). El modelo L-MEB (L-band Emission of the Biosphere) es la base de los algoritmos de nivel 2 (L2) y 3 (L3) de SMOS (Kerr et al., 2012). En ambos algoritmos, los parámetros del modelo de transferencia radiativa (Mo et al., 1982) relativos a la rugosidad del suelo y la vegetación, se consideran invariables en el tiempo y su valor viene dado por el tipo de cobertura vegetal siguiendo la clasificación de ECOCLIMAP (Masson et al., 2003). Los productos de SMOS se dividen en varios niveles (del 1 al 4). El nivel 1 es el producto primario que corresponde a las medidas de temperatura de brillo realizadas por el radiómetro. Los niveles 2 y 3 ofrecen además del producto de temperatura de brillo, la humedad de suelo y espesor óptico de la vegetación, así como todos los datos auxiliares utilizados en el modelo. Los productos de nivel 2 y 3 están geo-referenciados y usan, respectivamente, la malla ISEA (Icosahedral Synder Equal Area), 4H9 (Talone et al., 2015) y EASE (Equal-Area Scalable Earth) 2.0 (Armstrong et al., 1997). El modelo L-MEB El modelo L-MEB (Wigneron et al., 2007) es la base de los algoritmos L2 y L3 de SMOS, en los cuales se estima la humedad del suelo y el espesor óptico de la vegetación a partir de las observaciones de satélite. L-MEB emplea datos multi-angulares de temperatura de brillo en polarización horizontal (H) y vertical (V) y un modelo iterativo que consiste en la minimización de una función de coste basada en la diferencia entre la temperatura de brillo observada y la simulada, para todos los ángulos disponibles. Esta función tiene también en cuenta la incertidumbre de los parámetros elegidos para su estimación (humedad del suelo y espesor óptico de la vegetación, en el caso de los algoritmos L2 y L3 de SMOS). L-MEB modela la emisión de la capa de suelo cubierta por vegetación, teniendo en cuenta las contribuciones del suelo, la vegetación y la radiación del cielo. El suelo se presenta como una superficie rugosa cubierta de vegetación. La temperatura de brillo simulada para un suelo cubierto de vegetación se expresa como suma de la emisión directa de la vegetación, la emisión del suelo atenuada por la capa vegetal y la emisión de la vegetación que es reflejada por el suelo y atenuada por la vegetación. La relación entre la humedad del suelo y la emisión del suelo vienen dadas por el modelo dieléctrico de Mironov et al. (2012) y las ecuaciones de Fresnel, donde la humedad del suelo y la constante dieléctrica del suelo están relacionadas con la reflectividad de una superficie plana. Los efectos de rugosidad del suelo se consideran mediante una aproximación semi-empírica, mientras que para la modelización de la vegetación se considera el modelo de transferencia radiativa τ-ω (Mo et al., 1982), donde τ es el espesor óptico de la vegetación y ω el albedo de dispersión simple de la vegetación. Parámetros de rugosidad del suelo y vegetación en L-MEB En banda L, la temperatura de brillo es muy sensible a la humedad del suelo, pero existen otros factores que perturban la señal y que deben tenerse en cuenta, tales como la temperatura del suelo y la vegetación (Wigneron et al., 2007), la textura, rugosidad del suelo (Wigneron et al., 2008) y la cubierta vegetal (Grant et al., 2007). El valor efectivo del albedo de dispersión simple tiene en cuenta los efectos de absorción y dispersión debidos a la cubierta vegetal (Kurum, 2013). En los algoritmos L2 y L3 de SMOS, el valor de es 0.06 ó 0.08 en bosques (Kerr et al., 2012) y cero en cubiertas vegetales de escasa vegetación. Este último valor está basado en el análisis de campañas de medidas en banda L (Wigneron et al., 2007) sobre ciertas áreas agrícolas y por lo tanto no es aplicable a todas las clases de vegetación. El estudio de a escala global es reducido y no existe un gran número de referencias al respecto. En el algoritmo de nivel 2 de SMAP, los valores de dependen del tipo de cobertura vegetal, variando de 0 a 0.08 (O’Neill et al., 2012), mientras que el producto de nivel 4 de SMAP proporciona, entre otros parámetros, estimaciones de a escala global (De Lannoy et al., 2014). Otro estudio que trata el parámetro a escala global es Konings et al. (2016), donde se muestra un mapa de valores de ω, con valores entre 0.02 y 0.04 para coberturas vegetales de escasa vegetación y ω = 0.03 – 0.06 en bosques. Por su parte, el estudio de Van Der Schalie et al. (2016) establece ω = 0.12 como el valor más representativo a escala global tras aplicar el algoritmo LPRM (Land Parameter Retrieval Model) sobre las observaciones de SMOS y comparando el resultado de humedad del suelo con diferentes modelos. Otros parámetros que caracterizan la vegetación en el algoritmo L-MEB son ttV and ttH. Estos parámetros cuantifican la influencia del ángulo de incidencia en el espesor óptico de la vegetación. Un estudio detallado de estos parámetros fue llevado a cabo por Schwank et al. (2012) en la Valencia Anchor Station demostrando que existen variaciones importantes en los valores de ttp (p = H, V) entre verano e invierno y también entre las polarizaciones vertical y horizontal. Sin embargo, a escala global estos parámetros son difíciles de estimar debido a la complejidad de los efectos del tronco de la planta, tallos, hojas y ramas, cuya orientación es altamente aleatoria. El valor de ttP en los algoritmos L2 y L3 de SMOS es invariable e igual a 1, suponiendo que la vegetación es isotrópica. Un valor de ttP > 1 o ttP < 1 supone asumir una distribución anisotrópica de la vegetación y conlleva, respectivamente, un incremento o un decremento de τ_P en función de θ. Para tener en cuenta los efectos de la rugosidad del suelo, los algoritmos L2 y L3 de SMOS incluyen cuatro parámetros (HR, QR, NRH and NRV) (Wigneron et al., 2007). El parámetro HR tiene en cuenta la disminución en la reflectividad del suelo debida a los efectos de rugosidad; QR parametriza los efectos de la polarización (mayor o menor influencia) y NRp (p = H, V) la dependencia de la reflectividad con el ángulo de incidencia. En ambos algoritmos (L2 and L3 de SMOS), el valor de QR se supone igual a cero de manera global, mientras que a NRH y NRV se les asignan los valores 2 y 0, respectivamente. Por su parte, los valores de HR vienen definidos en función de la clasificación de usos del suelo ECOCLIMAP, siendo HR = 0.3 en bosque y HR = 0.1 en el resto de suelos (Kerr et al., 2012). En el algoritmo de humedad del suelo de SMAP L2, el valor de HR es diferente según la clasificación IGBP (International Geosphere-Biosphere), mientras que NRp = 2 (p = H, V). A escala local, existen algunas referencias sobre el valor de los parámetros de rugosidad. Como ejemplo, el estudio de Wigneron et al. (2007) arroja valores de HR = 0.1 - 0.2 para cultivos de soja y trigo y ~ 0.7 para campos de maíz. En España, el estudio de Cano et al. (2010) estima el valor de HR ~ 0.35 sobre la vegetación de matorral mediterráneo, mientras que el parámetro QR se analiza en Lawrence et al. (2013), concluyendo que QR = 0 es un valor generalizable en ausencia de condiciones de rugosidad extremas. En lo que respecta a los parámetros NRH y NRV, Escorihuela et al. (2007) y Lawrence et al. (2013) proponen una diferencia de NRH – NRV ~ 2 para superficies de poco relieve y (~ 1 – 1.5) para suelos rugosos.The capability of L-band radiometry to monitor surface soil moisture (SM) at global scale has been analyzed in numerous studies, mostly in the framework of the Soil Moisture Ocean Salinity (SMOS) and near future SMAP (Soil Moisture Active Passive) space borne missions. While the soil moisture of the first centimeters of the soil surface (~3 cm) is strongly related to the Brightness Temperature (TB) measurements, other parameters must be accounted for in order to produce accurate estimations of SM. To retrieve SM from L-band radiometric observations, two significant effects have to be accounted for: soil roughness and vegetation. In the first part of this thesis, the effects of soil roughness on retrieved SM values were evaluated using in-situ observations acquired by the L-band ELBARA-II radiometer, over a vineyard field at the Valencia Anchor Station (VAS) site during the year 2013. In the SMOS algorithm, L-MEB (L-band Microwave Emission of the Biosphere) is the forward model. Different combinations of the values of the model parameters used to account for soil roughness effects (HR, QR, NRH and NRV) were evaluated. The evaluations were made by comparing in-situ measurements of SM (used here as a reference) against SM retrievals derived from tower-based ELBARA-II brightness temperatures. The general retrieval approach consists of the inversion of L-MEB. Two specific configurations were tested: the classical 2-Parameter (2-P) retrieval configuration [where SM and τ_NAD (vegetation optical depth at nadir) were retrieved] and a 3-Parameter (3-P) configuration, accounting for the additional effects of the vineyard vegetation structure. Using the 2-P configuration, it was found that setting NRP (P = H or V) equal to -1 produced the best SM estimations in terms of correlation and unbiased Root Mean Square Error (ubRMSE). The assumption NRV = NRH = -1 leads to a simplification in L-MEB, since the two parameters τ_NAD and HR can be grouped and retrieved as a single parameter (method defined here as the Simplified Retrieval Method (SRP)). A main advantage of the SRP method is that it is not necessary to calibrate the value of HR before performing SM retrievals. Using the 3-P configuration, improved results were obtained in the SM retrievals in terms of correlation and ubRMSE. Finally, it was found that the use of in-situ roughness measurements to calibrate the values of the roughness model parameters did not provide significant improvements in the SM retrievals in comparison with the SRP method. The second part of the thesis focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters in the SM retrieval at global scale. In the current SMOS Level 2 (L2) and Level 3 (L3) retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 - 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H, V) were tested. In addition, the inversion approach was simplified by considering the SMOS pixels as homogeneous instead of retrieving SM only over a fraction of the pixel (excluding forested areas), as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = -1 (p = H, V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08 - 0.12 and 0.1 - 0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics. This result is a key step in the calibration of the roughness and vegetation parameters of future versions of the operational SMOS retrieval algorithm. This result was also at the base of the development of the so-called SMOS-INRA-CESBIO (SMOS-IC) product. The SMOS-IC product provides daily values of the SM and τ_NAD parameters at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. SMOS-IC is much simpler and does not account for corrections associated to the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary data sets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (ω) and soil roughness parameters. An inter-comparison of the SMOS-IC and SMO3L3 products (V300) is presented in this thesis based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A 6 year (2010-2015) inter-comparison of the two SMOS products (SMOS-IC and SMOSL3 SM (V300)) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of τ_NAD, SMOS-IC was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and of the northern mid-latitudes. The SMOS-IC VOD product is now extensively used in applications analyzing the impact of droughts on vegetation carbon budgets/biomass at continental scales

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

Analyzing the impact of using the SRP (Simplified roughness parameterization) method on soil moisture retrieval over different regions of the globe

International audienceThis paper focuses on a new approach to account for soil roughness effects in the retrieval of soil moisture (SM) at L-band in the framework of the SMOS (Soil Moisture and Ocean Salinity) mission: the Simplified Roughness Parameterization (SRP). While the classical retrieval approach considers SM and τ nad (vegetation optical depth) as retrieved parameters, this approach is based on the retrieval of SM and the TR parameter combining τ nad and soil roughness (TR τ nad + Hr /2). Different roughness parameterizations were tested to find the best correlation (R), bias and unbiased RMSE (ubRMSE) when comparing homogeneous retrievals of SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) vineyard field. The highest R (0.68) and lowest ubRMSE (0.056 m3 m-3) were found using the SRP method. Using the SMOS observations comparisons against several SM networks were also made: AACES, SCAN, watersheds and SMOSMANIA. SM was retrieved over all these stations. The SRP and another similar approach (SRP2) improved the averaged ubRMSE, while the SRP2 method leaded to higher correlation values (R). A global underestimation of SM was noticed, which may be linked to the differences in the sampling depths of the L-band observations ( ~ 0-3 cm for both Elbara-II and SMOS) and of the in situ measurements ( ~ 0-5 cm)

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.The two-layer model of Shuttlerworth and Wallace (SW) was evaluated to estimate actual evapotranspiration (ETa) above a drip-irrigated Merlot vineyard, located in the Talca Valley, Region del Maule, Chile (35° 25' LS; 71° 32' LW ; 136m above the sea level). An automatic weather system was installed in the center of the vineyard to measure climatic variables (air temperature, relative humidity, and wind speed) and energy balance components (solar radiation, net radiation, latent heat flux, sensible heat flux, and soil heat flux) during November and December 2006. Values of ETa estimated by the SW model were tested with latent heat flux measurements obtained from an eddy-covariance system on a 30 minute time interval. Results indicated that SW model was able to predict ETa with a root mean square error (RMSE) of 0.44 mm d-1 and mean absolute error (MAE) of 0.36 mm d-1. Furthermore, SW model predicted latent heat flux with RMSE and MAE of 32 W m-2 and 19W m-1, respectively

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.A In order to promote irrigation sustainability through reporting by irrigation water managers around Australia, we have developed an adaptive framework and methodology for improved triple-bottom-line reporting. The Irrigation Sustainability Assessment Framework (ISAF) was developed to provide a comprehensive framework for irrigation sustainability assessment and integrated triple-bottom-line reporting, and is structured to promote voluntary application of this framework across the irrigation industry, with monitoring, assessment and feedback into future planning, in a continual learning process. Used in this manner the framework serves not only as a "reporting tool", but also as a "planning tool" for introducing innovative technology and as a "processes implementation tool" for enhanced adoption of new scientific research findings across the irrigation industry. The ISAF was applied in case studies to selected rural irrigation sector organisations, with modifications to meet their specific interests and future planning

USCID Fourth international conference on irrigation and drainage

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.Integrated regional water management -- Change of irrigation water quantity according to farm mechanization and land consolidation in Korea -- Local stakeholders participation for small scale water resources management in Bangladesh -- Water user participation in Egypt -- The man swimming against the stream knows the strength of it -- Roles and issues of Water Users' Associations for Sustainable Irrigation and Drainage in the Kyrgyz Republic and Uzbekistan in Central Asia -- Chartered Water User Associations of Afghanistan -- Updated procedures for calculating state-wide consumptive use in Idaho -- Measuring and estimating open water evaporation in Elephant Butte Reservoir in New Mexico -- Evapotranspiration of deficit irrigated sorghum and winter wheat -- Evaluation of a two-layer model to estimate actual evapotranspiration for vineyards -- Estimating pecan water use through remote sensing in Lower Rio Grande -- Estimating crop water use from remotely sensed NDVI, crop models, and reference ET -- Alfalfa production using saline drainage water -- Performance evaluation of subsurface drainage system under unsteady state flow conditions in coastal saline soils of Andhrapradesh, India -- Management strategies for the reuse of wastewater in Jordan -- Providing recycled water for crop irrigation and other uses in Gilroy, California -- Oakdale Irrigation District Water Resources Plan -- Use of information technology to support integrated water resources management implementation -- Decision-support systems for efficient irrigation in the Middle Rio Grande -- Salt management -- Ghazi Barotha Project on Indus River in Pakistan -- Field tests of OSIRI -- Water requirements, irrigation evaluation and efficiency in Tenerife's crops (Canary Islands, Spain) -- Using wireless technology to reduce water use in rice production -- Variability of crop coefficients in space and time -- Assessing the implementation of integrated water management approach in closed basins -- New strategies of donors in the irrigation sector of Africa -- Holistic perspective for investments in agricultural drainage in Egypt -- Mapping system and services for canal operation techniques -- An open channel network modernization with automated structures -- Canal control alternatives in the irrigation district 'Sector BXII del Bajo Guadalquivir,' Spain -- Hydrodynamic behavior of a canal network under simultaneous supply and demand based operations -- Simulation on the effect of microtopography spatial variability on basin irrigation performance -- Drip irrigation as a sustainable practice under saline shallow ground water conditions -- Water retention, compaction and bean yield in different soil managements under a center pivot system -- Precision mechanical move irrigation for smallholding farmers -- Wild flood to graded border irrigation for water and energy conservation in the Klamath basin -- A method describing precise water application intensity under a CPIS from a limited number of measurements -- An irrigation sustainability assessment framework for reporting across the environmental-economic-social spectrum -- Planning for future irrigation landscapes -- One size does not fit all -- Water information networks -- Improving water use efficiency -- Irrigation system modernization in the Middle Rio Grande Valley -- Relationship of operation stability and automatic operation control methods of open canal -- Responsive strategies of agricultural water sector in Taiwan -- Effect of network water distribution schedule and different on-farm water management practices on sugarbeet water use efficiency -- Variable Frequency Drive (VFD) considerations for irrigation -- Accuracy of radar water level measurements -- Transition submergence and hysteresis effects in three-foot Cutthroat flumes -- Practical irrigation flow measurement and control -- Linear anionic PAM as a canal water seepage reducing technology -- In-situ non-destructive monitoring of water flow in damaged agricultural pipeline by AE -- Reoptimizing global irrigation systems to restore floodplain ecosystems and human livelihoods -- Water management technologies for sustainable agriculture in Kenya -- Impacts of changing rice irrigation practices on the shallow aquifer of Nasunogahara basin, Japan -- Drought protection from an in-lieu groundwater banking program -- Development of agricultural drought evaluation system in Korea -- Bean yield and root development in different soil managements under a center pivot system -- Can frost damage impact water demand for crop production in the future? -- Real time water delivery management and planning in irrigation and drainage networks -- Growth response of palm trees to the frequency of irrigation by bubblers in Khuzestan, Iran -- Application of Backpropagation Neural Network to estimate evapotranspiration for ChiaNan irrigated area, Taiwan -- Increasing water and fertilizer use efficiency through rain gun sprinkler irrigation in sugar cane agriculture

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Salt management is a critical component of irrigated agriculture in arid regions. Successful crop production cannot be sustained without maintaining an acceptable level of salinity in the root zone. This requires drainage and a location to dispose drainage water, particularly, the salts it contains, which degrade the quality of receiving water bodies. Despite the need to generate drainage water to sustain productivity, many irrigation schemes have been designed and constructed with insufficient attention to drainage, to appropriate re-use or disposal of saline drainage water, and to salt disposal in general. To control the negative effects of drainage water disposal, state and federal agencies in several countries now are placing regulations on the discharge of saline drainage water into rivers. As a result, many farmers have implemented irrigation and crop management practices that reduce drainage volumes. Farmers and technical specialists also are examining water treatment schemes to remove salt or dispose of saline drainage water in evaporation basins or in underlying groundwater. We propose that the responsibility for salt management be combined with the irrigation rights of farmers. This approach will focus farmers' attention on salt management and motivate water delivery agencies and farmers to seek efficient methods for reducing the amount of salt needing disposal and to determine methods of disposing salt in ways that are environmentally acceptable

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.Since 3000 BC, rice has been the main crop in the Korean Peninsula, and where currently most of the available irrigation water is used to grow paddy rice. Methods for calculating the quantity of irrigation water required developed in the 1990's were compared to quantities measured in the field. The largest difference between calculated and measured quantities occurred in April and May. Based on field data we obtained in the middle part of the Korean Peninsula, significant changes have occurred in rice management, which has changed the amount of irrigation water required. Rice is now transplanted earlier, and duration of the transplanting phase on the regional scale is shorter through mechanization and consolidation of land holdings. These changes need to be taken into account when calculating the quantity of water needed for irrigation

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.Experiences establishing Water User Associations (WUAs) in Egypt have been carried out for the past 15 years, with increasingly promising results. Most of these activities have been pilot projects aiming to demonstrate the benefits and sustainability of WUAs. They were consequently implemented through a centralized and resource-intensive process and focused on limited numbers of associations. Since 2003, the Ministry of Water Resources and Irrigation (MWRI) has adopted as policy the large-scale development of Branch Canal WUAs. With support from USAID, about 600 branch canal WUAs (BCWUAs) have since been established, covering 15% of Egypt's irrigated area and involving half a million farmers and residents. In order to achieve this impressive outcome, a different approach has been developed and implemented, emphasizing the direct involvement of MWRI field staff and a partnership between water users and MWRI managers. This paper also argues that the conventional approach of forming WUAs by focusing on water users, and empowering them to take over the O&M responsibilities of irrigation systems, is not adapted to the Egyptian context