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Abstract. Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Because of the the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still hampered by the lack of information about dynamic crop rotations or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. The aim of this study is to optimize a land modeling system, consisting of the Noah-MP LSM, coupled with a backscatter observation operator, over irrigated land in order to simulate backscatter predictions. This is a first step towards building a reliable data assimilation system to ingest level-1 Sentinel-1 observations. In this context, we tested how well modeled soil moisture and vegetation estimates from the Noah-MP LSM running within the NASA Land Information System (LIS), with or without irrigation simulation, are able to capture the signal of high-resolution Sentinel-1 backscatter observations over the Po river Valley, an important agricultural area in Northern Italy. Next, aggregated 1-km Sentinel-1 backscatter observations were used to calibrate a Water Cloud Model (WCM) as observation operator using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that activating an irrigation scheme provides the optimal calibration of the WCM, even if the irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chance of having error cross correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture and vegetation estimates via future data assimilation.

<p>Worldwide, the amount of water used for agricultural purposes is rising because of an increasing food demand. In this context, the detection and quantification of irrigation is crucial, but the availability of ground observations is limited. Therefore, an increasing number of studies are focusing on the use of models and satellite data to detect and quantify irrigation. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still characterized by simplifying assumptions, such as the lack of dynamic crop information, the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining models and satellite information through data assimilation can offer a viable way to quantify the water used for irrigation.</p><p>The aim of this study is to test how well modelled soil moisture and vegetation estimates from the Noah-MP LSM, with or without irrigation parameterization in the NASA Land Information System (LIS), are able to mimic in situ observations or to capture the signal of high-resolution Sentinel-1 backscatter observations in an irrigated area. The experiments were carried out over select sites in the Po river Valley, an important agricultural area in Northern Italy. To prepare for a data assimilation system, Level-1 Sentinel-1 backscatter observations, aggregated and sampled onto the 1 km EASE-v2 grid, were used to calibrate a Water Cloud Model (WCM) using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP. Results demonstrate that the use of the irrigation scheme provides the optimal calibration of the WCM, confirming the ability of Sentinel-1 to track the impact of human activities on the water cycle. Additionally, a first data assimilation experiment demonstrates the potential of Sentinel-1 backscatter observations to correct errors in Land Surface Model (LSM) simulations that are caused by unmodelled or wrongly modelled irrigation.</p>

Soils and vegetation were characterized at sites distributed over a 27-mile section of Nevada Route 77 and Arizona Route 68 north of the Mohave Generating Station in southern Nevada. Vegetation was also analyzed at two sites along the Colorado River, near River Bend. Sites ranged from 168 to 1069 m in elevation. The soils of this area are generally young and much of their morphology is inherited from parent material. Soils have developed under conditions of high temperatures and low rainfall. Most profiles examined were developed on alluvial deposits containing unconsolidated parent materials low in clay content. Some soils contained restrictive layers of caliche hardpan. Generally, soils were unleached and exhibited a high base status. Vegetation on sites below 800 m (bajadas, alluvial fans, washes) 4 was characteristic of the Mohave Desert. Aggregate densities were some thousands of plants per ha, total coverage was around 10-20%, A and diversities were low (H'<1.0). Based on importance value indexes, the dominant plants were creosotebush (Larrea tridentata) and bursage (Ambrosia dumosa), while other species assumed subdominant roles depending on particular sites (f.6., Krameria grayi, Encelia farinosa, Ephedra sp., and various eacti). Analyses were made of .Zn, Cu, B, V, Cr, Cd, Ni, Pb and Sri in samples of plants collected from sites believed to be exposed to < fallout from the station and from more remote control areas. Unusually high levels of Zn, Cu, B, Cd, Ni, Pb and Sn were detected in some V " samples-levels higher than those normally associated with root uptake. These higher levels were measured in samples from both exposed and control areas. Deposition of airborne particulate material including various trace metals was inferred, but the role of emissions from the Mohave Generating Station vis k vis other sources was not determined. Amounts of trace metals measured at sites along Route 77 and 68 were not obviously correlated with deposition rates predicted by an atmospheric transport model, but the number of samples considered was small.