El Oeste de la región Pampeana es conocido por su alternancia entre ciclos de inundación y sequías, enfrentando importantes desafíos relacionados a la gestión del agua. En este sentido, comprender la relación entre los fenómenos hidrológicos extremos y los excesos y déficits de precipitación acumulados resulta relevante para la planificación y gestión territorial. En este estudio se analizó la relación entre la variabilidad hidrológica y la variabilidad climática en el Oeste de la región Pampeana, desde el año 2000 hasta el 2023. Para este análisis se construyeron series temporales mensuales de anomalías de almacenamiento de agua terrestre (GRACE/GRACE-FO), de cobertura superficial de agua (Landsat) y del índice estandarizado de precipitación-evapotranspiración (SPEI) a múltiples escalas temporales (1 a 48 meses). A partir del método de nivel de umbral se identificaron tres episodios de inundación y dos de sequías. Se evidenció que las condiciones acumuladas extremadamente húmedas conjuntamente con niveles altos de almacenamiento de agua terrestre pueden generar inundaciones severas. Por otra parte, las altas correlaciones entre SPEI de 36 y 48 meses con el almacenamiento de agua terrestre (r = 0.85; p < 0,01) demostraron la importancia de los excesos y déficits de precipitación acumulados en el tiempo para comprender la sucesión de episodios de inundaciones y sequías, así como también la alta capacidad de los índices para monitorear el estado hidrológico de la región. Estos hallazgos resaltan la relevancia de las observaciones satelitales para mejorar la gestión y reducción de los riesgos asociados a las inundaciones y sequías.

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands.

The spatial monitoring of crop water status is crucial for agricultural purposes. The aim of this study was to analyze the sensitivity and response times of land surface temperature (LST) and the Normalized Difference Water Index (NDWI) to changes in soil moisture (VWC) in barley and wheat crops at plot scale in southeastern Argentine Pampas. VWC and LST were recorded daily by stations installed over the crops, and NDWI from Sentinel 2 (S2). Results showed instantaneous correlations between VWC and LST (r>-0.65) for soil depths explored by crop roots (20-50 cm). Correlations with NDWI were negative, which lacks biophysical meaning from a soil-plant system perspective. However, this index showed lower maximum values during the dry campaign. During the critical period of crops, the relationship between NDWI and VWC at 10 cm was positive, with a high correlation (r≈0.92) when considering responses with a 1-day time lag. This study contributes to the understanding of crops spectral behavior in the optical and thermal spectrum according to their water status, which is key to evaluating the usefulness of these data for monitoring agricultural systems.

Argentina is one of the main producers and exporters of grains and oilseeds, ranking third in soybean exports and fourth in barley ones. The 90% of this production occurs within the Argentine Pampas region (APR) under rainfed conditions, but its water consumption and pollution has not been studied in depth. Likewise, the link between soil moisture (SM) and Water Footprint (WF) generation is poorly studied at the global level. And yet, SM is a critical factor for the development of rainfed crops. This study aims to evaluate, at plot scale, the role of SM in the generation of the green (WFgreen) and grey (WFgrey) (WF). Additionally, it estimates the WF for rainfed barley and soybean crops in the Southeast of APR, where there are no reference values. Yields, water consumption and nitrogen (N) pollution load were estimated for different campaigns. Field data (weather, crop and production management) recorded in the study plots were used. Results indicated an average WFgreen of 1236 m3/t for soybeans and a WFgreen of 349 m3/t and WFgrey of 547 m3/t for barley. The study highlights the critical role of SM in both WF sub-indicators. Soil water availability, based on the evaporative fraction during critical growth stages, influenced yields and final WFgreen volumes. In addition, there was an effect on N uptake by crops. In the driest barley campaign, WFgrey increased by 234%. Insufficient SM restricted nutrient uptake, reducing yields and increasing N with the potential to leach or runoff. Consequently, it is suggested to adjust the WFgrey methodology incorporating SM fluctuations and unaccounted N losses. The study contributes to understand the WF drivers and highlights the need to assess them accurately. In particular, it aims to reduce the gaps surrounding the water consumption of rainfed crops, thereby supporting resource conservation and grain provisioning efforts.

This study presents an initial investigation into crop (barley and soybean) monitoring using vegetation optical depth (VOD) retrieved from the NASA Soil Moisture Active Passive mission (SMAP). VOD was consistent with Normalized Difference Vegetation Index (NDVI) and latent heat flux (LE) from Moderate-Resolution Imaging Spectroradiometer (MODIS) within the southeastern area of the Argentine Pampas. Pre-liminary results reveal the potential of L-band VOD as a tool for monitoring water crops condition, demonstrating relevant synergies with traditional vegetation indicators.

An important issue for agricultural planning is to estimate evapotranspiration accurately due to its fundamental role in sustainable use of water resources. It is essential to have reliable and precise evapotranspiration (ET) measurements to improve models or products. This work aims to evaluate a generalized linear model (GLM) in order to estimate actual evapotranspiration of barley crop with satellite (Landsat, Sentinel, and CERES) and reanalysis (MERRA-2) data. The results obtained were compared with water balance values from an agrometeorological station. The GLM with the combination of MERRA-2/CERES/Sentinel 2 as input was the best performance (R2 = 0.59). The results show the feasibility of applying machine learning algorithms for obtaining actual evapotranspiration values in agricultural plains without ground agro-meteorological data.

The Irrigation and Drainage Consortium of Villa Regina, Río Negro is part of a vast, irrigated valley. Its main production is pears and apple crops. Throughout the years, water and drainage problems have been detected due to a water table raise. Studying the zone extensively becomes crucial in order to understand the groundwater and surface water sources interaction and remote sensing information may enable the analysis and understanding of the hydrological behavior of the basin. The aim of this study is to detect variations in the surface water area in the Río Negro in years with contrasting hydrological conditions by using spectral indexes and relate them to water table levels. Sentinel 2 images were used in order to analyze the surface water area. Spectral indexes NDWI and MNDWI were calculated for periods of different flows: 318.66 m3/s (August 2017) y 1157.18 m3/s (August 2018). Unconfined aquifer piezometers near to the river were selected to elaborate piezometric graphs and calculate water table average elevation. The NDWI and the MNDWI indexes were consistent with the river flood in 2018, showing an increase of the water covered area of 420,000 m2 with regard to 2017. The analysis of piezometric graphs during the specific period showed an average elevation of the water table level of 0.7 m. These results suggest the interaction between the river flood and the water table in the studied area. It is possible to conclude that the use of geographical information can contribute to analyze and understand this interaction and can be used as a complement to design proper integrated water management programs for the Alto Valle of Rio Negro and Neuquén.

Evapotranspiration is a key variable of the water cycle. Its calculation requires several ground data that frequently are not available. This study contains a detailed method and measurements of meteorological and energy balance variables that can be used to estimate the daily actual evapotranspiration (ETa). A linear generalized model is obtained to calculate the ETa from common variables measured in meteorological stations. The method showed a good performance over a barley crop of easthern Argentine Pampas and can be applied and tested in other great plains. Measurements of soil-plant-atmosphere are included The routines to reproduce the method are included The generalized method allows the calculation of daily ETa over crops and was tested over barley crops.

Climate services provide information on El Niño-Southern Oscillation (ENSO) evolution and predicted seasonal precipitation, broadly used by decision makers from the agriculture sector. However, soil moisture participates in a more complex soil–atmosphere interaction at subseasonal scale. This work aims to identify the ENSO signal on subseasonal precipitation indices and to assess soil moisture response during austral spring–summer over the southern Argentine Pampas. From daily precipitation, 16 indices were analyzed, and the temperature vegetation dryness index (TVDI) was computed as representative of soil moisture availability. In general, the different precipitation indices presented coherence with wetter (drier) climate conditions under the El Niño (La Niña) phase. A strong signal was found for precipitation frequency in November and accumulation in December, whereas reversal and a weak signal was observed during January, crucial for summer crops. The analysis of soil–moisture interaction suggests that positive precipitation anomalies during El Niño can be reinforced by high soil moisture stored in previous months (e.g., during El Niño 2002–2003). The drying process increases in soils with low water retention capacity, producing a spatially heterogeneous impact (e.g., El Niño 2009–2010). The dry pattern expected for La Niña events was observed in 2007–2008, affecting regions with high water retention and productivity. In addition, long wet spells presented a stronger influence in these regions. Owing to the spatial and temporal heterogeneity observed at subseasonal scale, this study suggests the need for the joint analysis of atmospheric variables and soil moisture content for medium-term agricultural planning in the context of ENSO events in the southern Argentine Pampas.

Water availability for vegetation use has been associated with the relative amount of water in the plant and is a key factor for modeling variables related to the soil-plant system (e.g., net primary production, drought effects on vegetation). To the best of our knowledge, the integration of spectral proxies of vegetation water content (near-infrared (NIR), shortwave-infrared (SWIR) bands) and land surface temperature (LST) for estimation, not only of vegetation water content but also soil water available for the evapotranspiration process requires more analysis. This study aims to assess the relationship between NIR, SWIR reflectance, and LST data as indicators of water availability for crop use. For this purpose, vegetation water content, LST, and spectral reflectance over soybean, corn, and barley were measured in the field and the laboratory. Based on the consistency of satellite data from Moderate-Resolution Imaging Spectroradiometer (MODIS/Aqua) in relation to such measurements, a model is proposed, which can be parameterized from remotely sensed NIR-SWIR/LST scatterplots. The obtained results were tested in the Argentine Pampas, showing coherence with surface processes at regional scale associated with soil water availability. The comparison with soil moisture at different depths (R2 > 0.7) showed that the method is sensitive to variations in root zone water availability. Given the reliance of the index on just satellite data, it can be pointed that the potential not only for vegetation water stress analyses but also in the context of hydrological modeling as an input of water availability.

Agriculture is among the main causes of water pollution. Currently, 75% of global anthropogenic nitrogen (N) loads come from leaching/runoff from cropland. The grey water footprint (GWF) is an indicator of water resource pollution, which allows for the evaluation and monitoring of pollutant loads (L) that can affect water. However, in the literature, there are different approaches to estimating L and thus contrasting GWF estimates: (A1) leaching/runoff fraction approach, (A2) surplus approach and (A3) soil nitrogen balance approach. This study compares these approaches for the first time to assess which one is best adapted to real crop production conditions and optimises GWF calculation. The three approaches are applied to assess N-related GWF in barley and soybean. For barley in 2019, A3 estimated a GWF value 285 to 196% higher than A1, while in 2020, the A3 estimate was 135 to 81% higher. Soybean did not produce a GWF due to the crop characteristics. A3 incorporated N partitioning within the agroecosystem and considered different N inputs beyond fertilization, improving the accuracy of L and GWF estimation. Providing robust GWF results to decision-makers may help to prevent or reduce the impacts of activities that threaten the world’s water ecosystems and supply.

The vegetation water status is a crucial variable for modelling of drought impact, vegetation productivity and water fluxes. Methods for spatial estimation of this variable still need to be improved. The integration of remotely sensed data of land surface temperature (LST) and water vegetation indices based on near-infrared (NIR) and short-wave infrared (SWIR) reflectance for estimation of vegetation water content and water available for evapotranspiration require more analysis. This study contains a detailed method and measurements of LST, NIR and SWIR reflectance of soybean, corn and barley taken in field campaigns in central Argentine Pampas and laboratory with a ST PRO Raytek (8-14 mu m) and a spectrometer SVC HR-1024i (0.35 and 2.5 mu m). Also, relative water content of leaves was measured in laboratory during the dehydration process. This method and dataset could be also used for researching other wavelengths between 0.35 and 2.5 mu m as indicator of water vegetation status (e.g. solar-induced chlorophyll fluorescence, photosynthesis). Procedures useful to measure field spectra of vegetation are presented. Not only the traditional method to measure leaves spectra in laboratory, but also in field were applied. The method allows the integration of spectra and thermal data as a proxy of vegetation water status. (C) 2020 The Author(s). Published by Elsevier B.V.

Fil: Irigoyen, A.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina

Evapotranspiration is an important indicator for the management and planning of water resources. The estimation of evapotranspiration is usually done trough bio-physical modeling, which requires the observation of multiple variables as well as the definition of the corresponding equations. In this paper, we evaluate the use of supervised machine learning as a strategy to get estimates of evapotranspiration from data observed in multiple meteorological stations in the Pampean region of Argentina. Particularly, we evaluate and compare regression methods for estimating the evapotranspiration from data collected during an extensive period of time, more than 40 years, from 24 stations placed in the region under study. The results obtained thus far are promising as they show the feasibility of applying a machine learning approach for obtaining accurate evapotranspiration estimates. The practical implications of these findings are relevant for the design of more efficient water monitoring systems in the country.

As the collection of soil moisture data is often costly, it is essential to implement data‐worth analysis in advance to obtain a cost‐effective data collection scheme. In previous data‐worth analysis, the model structural error is often neglected. In this paper, we propose a robust data‐worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data‐worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data‐worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data‐worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy‐to‐obtain meteorological data into GP training yielded better data‐worth assessment.

Agriculture accounts for about 70% of the fresh water use in the world, dominating rainfed production systems. As meeting future food demand will require an increase in crop production, new techniques are necessary to monitor the spatial variability of agricultural water use. However, the use of remote sensing for the water footprint estimation is limited. This study aims at evaluating the spatial variability of the soil-water consumption in soybean crops, also termed as green water footprint (WFgreen), in a sector of the Argentine Pampas using satellite data. WFgreen was evaluated at spatial resolution of 250 m, estimating the soil water availability through the evaporative fraction and crop yield from Moderate-Resolution Imaging Spectroradiometer (MODIS/Aqua) data. In the analysed soybean plots, the WFgreen, varied from 900 m(3) t t(-1) 1800m(3) t(-1). The preliminary comparison of the method with field measurements showed a RMSE = 494 m(3) t(-1) and Bias =-410 m(3) t(-1), respectively. The high spatial variability reflected the heterogeneity of soil-water use efficieny. The proposed technique can be useful to obtain WFgreen maps at medium spatial resolutions (250 m-1000 m). Also, it can be applied in regions with poor ground data coverage to estimate the WFgreen, after a parameterization of the model. The contribution to our understanding of the relationship between soil-water availability, rainfed-crop productivity and then WFgreen is expected. (C) 2020 Elsevier B.V. All rights reserved.

Attributing to the flexibility in considering various types of observation error and model error, data assimilation has been increasingly applied to dynamically improve soil moisture modeling in many hydrological practices. However, accurate characterization of model error, especially the part caused by defective model structure, presents a significant challenge to the successful implementation of data assimilation. Model structural error has received limited attention relative to parameter and input errors, mainly due to our poor understanding of structural inadequacy and the difficulties in parameterizing structural error. In this paper, we present a dynamic data-driven approach to estimate the model structural error in soil moisture data assimilation without the need for identifying error generation mechanism or specifying particular form for the error model. The error model is based on the Gaussian process regression and then integrated into the ensemble Kalman filter (EnKF) to form a hybrid method for dealing with multi-source model errors. Two variants of the hybrid method in terms of two different error correction manners are proposed. The effectiveness of the proposed method is tested through a suit of synthetic cases and a real-world case. Results demonstrate the potential of the proposed hybrid method for estimating model structural error and providing improved model predictions. Compared to the traditional EnKF without explicitly considering the model structural error, parameter compensation issue is obviously reduced and soil moisture retrieval is substantially improved.

The understanding of the hydrology of plain basins may be improved by the combined analysis of rainfall–run‐off records and remote sensed surface moisture data. Our work evaluates the surface moisture area (SMA) produced during rainfall–run‐off events in a plain watershed of the Argentine Pampas Region, and studies which hydrological variables are related to the generated SMA. The study area is located in the upper and middle basins of the Del Azul stream, characterized by the presence of small gently hilly areas surrounded by flat landscapes. Data from 9 rainfall–run‐off events were analysed. MODIS surface reflectance data were processed to calculate SMA subsequent to the peak discharge (post‐SMA), and previous to the rainfall events (prev‐SMA), to consider the antecedent wetness. Rainfall–run‐off data included total precipitation depth (P), maximum intensity of rainfall over 6 hr (I6max), surface run‐off registered between the beginning of the event and the day previous to the analysed MODIS scene (R), peak flow (Qp), and flood intensity (IF). In contrast with other works, post‐SMA showed a negative relationship with the R. Three groups of cases were identified: (a) Events of low I6max, high prev‐SMA, and low R were associated with slow and weakly channelized flow over plain areas, leading to saturated overland flow (SOF), with large SMA; (b) events of high I6max, low prev‐SMA, and medium to high R were rapidly transported along the gentle slopes of the basin, related to Hortonian overland flow (HOF) and low post‐SMA; and (c) events of medium to high I6max and prev‐SMA with medium R were related to heterogeneous input‐antecedent‐run‐off conditions combined: Local spatial conditions may have produced HOF or SOF, leading to an averaged response with medium SMA. The interactions between the geomorphology of the basin, the characteristics of the events, and the antecedent conditions may explain the obtained results. This analysis is relevant for the general knowledge of the hydrology of large plains, whose functioning studies are still in their early stages.