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Abstract Quantitative estimations of ecohydrological water partitioning into evaporation and transpiration remains mostly based on plot‐scale investigations that use well‐instrumented, small‐scale experimental catchments in temperate regions. Here, we attempted to upscale and adapt the conceptual tracer‐aided ecohydrology model STARRtropics to simulate water partitioning, tracer, and storage dynamics over daily time steps and a 1‐km grid larger‐scale (2565 km²) in a sparsely instrumented tropical catchment in Costa Rica. The model was driven by bias‐corrected regional climate model outputs and was simultaneously calibrated against daily discharge observations from 2 to 30 years at four discharge gauging stations and a 1‐year, monthly streamwater isotope record of 46 streams. The overall model performance for the best discharge simulations ranged in KGE values from 0.4 to 0.6 and correlation coefficients for streamflow isotopes from 0.3 to 0.45. More importantly, independent model‐derived transpiration estimates, point‐scale residence time estimates, and measured groundwater isotopes showed reasonable model performance and simulated spatial and temporal patterns pointing towards an overall model realism at the catchment scale over reduced performance in the headwaters. The simulated catchment system was dominated by low‐seasonality and high precipitation inputs and a marked topographical gradient. Climatic drivers overrode smaller, landcover‐dependent transpiration fluxes giving a seemingly homogeneous rainfall‐runoff dominance likely related to model input bias of rainfall isotopes, oversimplistic Potential Evapotranspiration (PET) estimates and averaged Leaf Area Index (LAI). Topographic influences resulted in more dynamic water and tracer fluxes in the headwaters that averaged further downstream at aggregated catchment scales. Modelled headwaters showed greater storage capacity by nearly an order of magnitude compared to the lowlands, which also favoured slightly longer residence times (>250 days) compared to superficially well‐connected groundwater contributing to shorter streamflow residence times (<150 days) in the lowlands. Our findings confirm that tracer‐aided ecohydrological modelling, even in the data‐scarce Tropics, can help gain a first, but crucial approximation of spatio‐temporal dynamics of how water is partitioned, stored and transported beyond the experimental catchment scale of only a few km². 

Large-scale continuous crop monitoring systems (CMS) are key to detect and manage agricultural production anomalies. Current CMS exploit meteorological and crop growth models, and satellite imagery, but have underutilized legacy sources of information such as operational crop expert surveys with long and uninterrupted records. We argue that crop expert assessments, despite their subjective and categorical nature, capture the complexities of assessing the “status” of a crop better than any model or remote sensing retrieval. This is because crop rating data naturally encapsulates the broad expert knowledge of many individual surveyors spread throughout the country, constituting a sophisticated network of “people as sensors” that provide consistent and accurate information on crop progress. We analyze data from the US Department of Agriculture (USDA) Crop Progress and Condition (CPC) survey between 1987 and 2019 for four major crops across the US, and show how to transform the original qualitative data into a continuous, probabilistic variable better suited to quantitative analysis. Although the CPC reflects the subjective perception of many surveyors at different locations, the underlying models that describe the reported crop status are statistically robust and maintain similar characteristics across different crops, exhibit long-term stability, and have nation-wide validity. We discuss the origin and interpretation of existing spatial and temporal biases in the survey data. Finally, we propose a quantitative Crop Condition Index based on the CPC survey and demonstrate how this index can be used to monitor crop status and provide earlier and more precise predictions of crop yields than official USDA forecasts released midseason. 

Abstract. The acceleration of urbanization requires sustainable, adaptive management strategies for land and water use in cities. Although the effects of buildings and sealed surfaces on urban runoff generation and local climate are well known, much less is known about the role of water partitioning in urban green spaces. In particular, little is quantitatively known about how different vegetation types of urban green spaces (lawns, parks, woodland, etc.) regulate partitioning of precipitation into evaporation, transpiration and groundwater recharge and how this partitioning is affected by sealed surfaces. Here, we integrated field observations with advanced, isotope-based ecohydrological modelling at a plot-scale site in Berlin, Germany. Soil moisture and sap flow, together with stable isotopes in precipitation, soil water and groundwater recharge, were measured over the course of one growing season under three generic types of urban green space: trees, shrub and grass. Additionally, an eddy flux tower at the site continuously collected hydroclimate data. These data have been used as input and for calibration of the process-based ecohydrological model EcH2O-iso. The model tracks stable isotope ratios and water ages in various stores (e.g. soils and groundwater) and fluxes (evaporation, transpiration and recharge). Green water fluxes in evapotranspiration increased in the order shrub (381±1mm) 

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California.