Abstract Coastal and inland communities are increasingly vulnerable to tropical cyclones (TCs), yet accurately assessing flood risk remains challenging due to uncertainties in precipitation inputs. This study evaluates how three gridded precipitation products, gauge based [Gridded Cooperative Observer Program (GCOOP)], radar based (Stage IV), and satellite based [Integrated Multi-satellitE Retrievals for GPM (IMERG)], influence surface hydrology, specifically total runoff, streamflow volume, and timing of peak flow through a case study of TC Beryl 2012 in the South Atlantic–Gulf region. Using the Variable Infiltration Capacity (VIC) model and a GIS routing model, we evaluate how biases in daily precipitation estimates propagate into streamflow outputs. While precipitation differences of up to 186 mm in storm totals among datasets had minimal effect on peak streamflow timing, however, the peak discharge magnitudes varied with relative errors of 24% and 12% for IMERG compared to GCOOP and Stage IV, respectively. Results indicate that surface runoff showed high sensitivity to precipitation variability, while base flow and evapotranspiration exhibited buffering effects. The watershed-scale integration dampened local precipitation variability, resulting in a 2.4 cm day −1 streamflow difference between GCOOP and IMERG, which is acceptable for regional flood assessment. These findings show that, for this drought-preconditioned watershed and TC event, satellite precipitation products provide reasonable streamflow estimates, particularly when peak timing is more critical than absolute magnitude. Further evaluation across diverse TC events and watershed conditions is needed before broader operational conclusions can be drawn. Significance Statement Reliable and timely flood forecasts are vital during hurricanes, yet many vulnerable regions lack dense ground-based rainfall networks. Our previous research found striking differences in rainfall estimates for the same hurricane among three products using different algorithms. This raised an intriguing question—does this variability in rainfall measurements propagate to the flooding estimation during extreme events like hurricanes? Using Tropical Cyclone (TC) Beryl (2012) as a case study, we find that despite large rainfall differences, all three products predicted flood arrival times within hours of each other. Satellite data underestimated flood peaks by only 12%–24%. Natural watershed processes smooth out local rainfall variations, making satellites more reliable than expected. These findings support using satellite data for flood prediction in vulnerable regions lacking ground observations.

Study region: Department of Arequipa in Peru Study focus: Throughout the coastal department of Arequipa, Peru, extensive water infrastructure is used to redistribute water from the Andes highlands to the coastal desert and altiplano for use by urban populations and agriculture. Here we build on earlier simulations in an unregulated headwater to explicitly model the impact of water management on streamflow throughout the basin. These simulations can be the basis of estimating naturalized streamflow for the support of native populations of sensitive species in the coastal river reaches and highlands. The goal was to quantify the effect of complex and pervasive hydrologic modification and provide a tool for local water managers to assess the impact on streamflow metrics that impact aquatic ecology. New hydrological insights for the region: Simulated hydrologic metrics along the Camana and Quilca-Chili Rivers show the complex interactive effects of the hydrologic modifications. Withdrawals for irrigation, the city, and especially transfers to other basins decrease low flows and increase flashiness, while the reservoirs reduce maximum flow and increase low flows. The hydrologic modifications are manifested differently at points along the stream channel which may have important implications for endemic aquatic species. An interactive online tool allows managers to assess the differences between naturalized and current flow at any location and visualize how current management is impacting the flow for specific reaches.

Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking.

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30-year (1988-2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS-TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30-year annual and monthly climate normals (ACM-Normals) and 30 years of annual, monthly, and daily climate maps (ACM-YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets.

Agricultural water management is increasingly prioritized throughout the world as producers are tasked with meeting growing crop demand while also managing environmental resources more sustainably. Likewise, agriculture is increasingly modifying the terrestrial water cycle. In response to these dynamics, the informal research discipline of agrohydrology continues to grow, fueled by a new era of rapidly evolving research tools and big data availability. While many researchers are actively invested in agrohydrology as a research topic, there remains a gap in formalizing this valuable discipline. This article aims to: (a) identify key research themes in agrohydrology, (b) conceptualize future research topics within each theme, and (c) estimate a timeframe before topics become pressing (i.e., before a topic becomes a limiting factor in advancing water management in an agricultural context). This commentary is meant to guide the trajectory of an evolving discipline of agrohydrology, the practice of agricultural water management at multiple nested scales, and the conversation of the invested public.

Changing temperatures and precipitation patterns from climate change are a major risk to crop yields. Producers have technology options for mitigating this risk with one such technology termed drainage water recycling (DWR). DWR involves diverting subsurface drainage water to ponds where it is stored for later irrigation. Crop insurance may interfere with DWR adoption by providing producers with another avenue to manage climate change risk. It is hypothesized that government-subsidized crop insurance reduces climate change technology adoption. Based on real options, this analysis considers two policy regimes: when crop insurance is in effect and not. In a Poisson jump process, it further considers the insurance effect of producers' returns jumping when facing a crop disaster. Results indicate crop insurance has a minimal effect on DWR adoption, and in most scenarios, the DWR adoption thresholds are too large for a producer to invest for climate change adaptation without additional financial incentives. The baseline DWR adoption scenario, with no crop insurance impact, requires revenue of $1,114/acre, or 1.57 times current conventional revenue.

Study region: Sierra Nevada de Santa Marta (SNSM) region of Colombia. Study focus: This research was conducted as a case study to generate relevant, quantitative information to support cacao farmer decision-making processes concerning water management in the SNSM. It involved the development and evaluation of a spatial dataset of precipitation and temperature, integration of digital soil mapping with a modification of the Thornthwaite and Mather water balance model, and finally an assessment of water sufficiency for cacao production. We elaborated site-specific and spatially-distributed analyses to generate information that will be shared with technicians who assist cacao growers in the SNSM. New hydrological insights for the region: Under the climate conditions for the analysis period (1989-2018), rainfall was not enough to prevent cacao yield losses for 10 out of the 27 farms evaluated. The location of farms in two departments with contrasting climate conditions showed the importance of spatial analysis of water availability when providing recommendations of management practices to cacao growers. The results revealed that farms facing less frequent water stress are characterized by higher rainfalls and lower temperatures, soils that contain more organic matter, and are located at higher elevations with steeper slopes. Temporally, water stress is highest in the months February-August, with special interest in March-April as the dry season ends and July-August just before the peak rainy season.

Crop evapotranspiration (ET), which is directly related to latent heat flux, is also a key indicator in determining the water status of crops. In order to estimate the latent heat flux, two-source energy balance (TSEB) models have been developed for thermal imagery from satellite platforms. However, because of the coarse resolution of thermal sensors on the satellite, distinguishing soil and vegetation is difficult which complicates the calculation process and introduces errors in latent heat estimates. In this research, high-resolution thermal datasets (0.05 m) and corresponding RGB datasets (0.03 m) were used for calculating crop latent heat flux using an adapted TSEB model. The RGB datasets were used for supervised classification of soil and vegetation, and the classification results were then used to filter the thermal mosaics to separate vegetation and soil temperatures. The vegetation temperature is used for calculating latent heat flux and the results are validated against the ground reference measurements of latent heat using a handheld porometer. The objective of this research is to introduce a workflow including an adapted TSEB model which is customized for high resolution thermal images from unmanned aircraft systems (UAS) to estimate the latent heat flux of row crops in agricultural fields. Nine dates of data collection in 2018 and 2020 have been evaluated and the root mean square error (RMSE) varies between 16 to 106 W/m(2) depending on the days after planting (DAP) and the time of measurement for each day. The results indicate that the workflow introduced here is able to provide estimates of instantaneous latent heat flux (evapotranspiration) measurements for row crops in agricultural fields which will enable people to make reliable decisions related to irrigation scheduling.

Efficiency of light interception, Radiation use efficiency and harvest index can be used as targets to improve grain yield potential in soybean. Grain yield (GY) production can be expressed as the result of three main efficiencies: light interception (Ei), radiation use (RUE), and harvest index (HI). Although dissecting GY through these three efficiencies is not entirely new, there is a lack of knowledge about the phenotypic variation, the genetic architecture, and the relative contribution of these three efficiencies on GY in soybean. This knowledge gap coupled with laborious phenotyping prevents the active consideration of these efficiencies into breeding programs. This study aims to reveal the phenotypic variation, heritability, genetic relationships, genetic architecture, and genomic prediction for Ei, RUE, and HI in soybean. We evaluated a maturity control panel of 383 Recombinant Inbred Lines (RILs) selected from the soybean nested association mapping (SoyNAM) population. Dry matter ground measured along with canopy coverage (CC) from UAS imagery were collected in three environments. Light interception was modeled through a logistic curve using CC as a proxy. The total above-ground biomass collected during the growing season and its respective cumulative light intercepted were used to derive RUE through linear models fitting. Additive-genetic correlations, genome-wide association (GWA) and whole-genome regressions (WGR) were performed to evaluate the relationship between traits, their association with genomic regions, and the feasibility of predicting these efficiencies with genomic information. Correlation analyses considered three groups: the entire data set, and the high- and low-yielding RILs to determine association as a function of the GY. Our results revealed moderate to high phenotypic variation for Ei, RUE, and HI with ranges of 8.5%, 1.1 g MJ−1, and 0.2, respectively. Additive-genetic correlation revealed a strong relationship of GY with HI and moderate with RUE and Ei when whole data set was considered, but negligible contribution of HI on GY when just the top 100 was analyzed. The GWA analyses showed that Ei is associated with three SNPs; two of them located on chromosome 7 and one on chromosome 11 with no previous quantitative trait loci (QTLs) reported for these regions. RUE is associated with four SNPs on chromosomes 1, 7, 11, and 18. Some of these QTLs are novel, while others are previously documented for plant architecture and chlorophyll content. Two SNPs positioned on chromosome 13 and 15 with previous QTLs reported for plant height and seed set, weight and abortion were associated with HI. WGR showed high predictive ability for Ei, RUE, and HI with maximum correlation ranging between 0.75 and 0.80. Future improvements in GY can be expected through strategies prioritizing Ei for short-term results when using high yielding germplasm and RUE for medium- and long-term outcomes. This work is a pioneer attempt to integrate traditional physiological traits into the breeding process in the context of physiological breeding.

Indigenous and local knowledge (ILK) systems are critical for achieving biodiversity conservation, climate change adaptation, and other environmental goals. However, ILK systems around the world are increasingly threatened by multiple stressors. Our study assesses the effect of climate change on ILK held by crop farmers in Peru's Colca Valley. We collected qualitative data on farmers' ILK through semi-structured interviews, which we supplemented with climatological trend analysis in four Colca Valley districts. We found that shifts in the rainy season together with warmer weather affected farmers' ILK, which was less effective for informing crop planting and irrigation practices in the context of climate uncertainty and unpredictability. Changing and uncertain ILK poses obstacles to adaptation strategies that require long-term institution building from local resource users, who may prioritize short-term solutions addressing urgent needs.

Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population ( n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high ( R 2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.

Changes to water resources are critical to all sectors of the economy. Climate change will affect the timing and quantity of water available in the environment as well as have an adverse effect on the quality of that water. Floods, droughts, and changing patterns of water scarcity—when water is not available in sufficient enough quantities or of a suitable quality at the right time to fulfill demand—are all critical factors when considering how and where Indiana will be able to economically develop in the future. Management of water resources will become even more important as different sectors try to minimize the risk of water scarcity in the face of increasing climate variability. This paper focuses on observed changes to Indiana’s water resources and how the availability and quality of those resources are likely to change in the face of future climate. Generally, Indiana is becoming wetter but with the projected increase coming primarily in the winter and spring. Summer water use will increase the likelihood of water shortages and the need for improved water management. In particular, Indiana may benefit from investment in methods to increase short-term storage of water—retaining more of the overabundance from winter and spring to relieve summer shortages.

Highlights A novel pixel-based calibration algorithm and an atmospheric correction method are developed. Application of the calibration methods reduces the RMSE of measurements to less than 1.32°C. The calibrations facilitate stitching of images together to form whole-field mosaics. Abstract . Thermal imagery can be used to provide insight into the water stress status and evapotranspiration demand of crops, but satellite-based sensors are generally too coarse spatially and too infrequent temporally to provide information of use for the management of specific fields. Thermal cameras mounted on small unmanned aerial systems (UAS) have potential to provide canopy temperature information at high spatial and temporal resolutions useful for crop management; however, without appropriate camera corrections, the measurement biases of these uncooled thermal cameras can be larger than ±5°C. Such uncertainty can render such camera measurements useless. In this research, a pixel-based (non-uniformity) calibration algorithm and an atmospheric correction method based on in-field approximate blackbody sources (water targets) were developed for a thermal camera. The objective was to improve the temperature measurement accuracy of the thermal camera on various land surfaces including soil and vegetation. With sufficient accuracy, temperature measurements can be used for the estimation of latent heat flux of field crops in the future. The thermal camera was first calibrated in a laboratory setting where the camera and environmental conditions were controlled. The results indicated that in the range between 10°C and 45°C, the calibrated temperatures were accurate, with an average bias of 1.76°C, and had a high linear correlation with reference temperatures (water target temperatures) (R2 > 0.99). Variability of measurements was also better constrained. In-field atmospheric correction is also important for obtaining high-accuracy thermal imagery. By applying both pixel-based calibration and atmospheric corrections, the RMSE (root mean square error) of validation targets from two dates in 2017 was reduced from 4.56°C and 6.36°C before calibration to 1.32°C and 1.24°C after calibration. The calibration process also increased the range of temperatures in the imagery, which enhanced contrast and may help with identification of tie-points and stitching of images together to form whole-field mosaics. Keywords: Atmospheric correction, Pixel-based calibration, Thermal remote sensing, UAS, Water targets.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Hydrological Assessment of Interconnected River Basins in Semi-Arid Region of Peruvian AndesAuthorsFariborzDaneshvariDJaneFrankenbergerKeithCherkaueriDHectorNovoaiDLauraBowlingSee all authors Fariborz DaneshvariDCorresponding Author• Submitting AuthorDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN. USAiDhttps://orcid.org/0000-0002-8375-4697view email addressThe email was not providedcopy email addressJane FrankenbergerDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN. USAview email addressThe email was not providedcopy email addressKeith CherkaueriDDepartment of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN. USAiDhttps://orcid.org/0000-0002-6938-5303view email addressThe email was not providedcopy email addressHector NovoaiDDepartamento de Ingeniería Civil, Universidad Nacional de San Agustín de Arequipa, PeruiDhttps://orcid.org/0000-0002-1333-8903view email addressThe email was not providedcopy email addressLaura BowlingDepartment of Agronomy, Purdue University, West Lafayette, IN. USAview email addressThe email was not providedcopy email address

Soil water dynamics are central in linking and regulating natural cycles in ecohydrology, however, mathematical representation of soil water processes in models is challenging given the complexity of these interactions. To assess the impacts of soil water simulation approaches on various model outputs, the Soil and Water Assessment Tool was modified to accommodate an alternative soil water percolation method and tested at two geographically and climatically distinct, instrumented watersheds in the United States. Soil water was evaluated at the site scale via measured observations, and hydrologic and biophysical outputs were analysed at the watershed scale. Results demonstrated an improved Kling-Gupta Efficiency of up to 0.3 and a reduction in percent bias from 5 to 25% at the site scale, when soil water percolation was changed from a threshold, bucket-based approach to an alternative approach based on variable hydraulic conductivity. The primary difference between the approaches was attributed to the ability to simulate soil water content above field capacity for successive days; however, regardless of the approach, a lack of site-specific characterization of soil properties by the soils database at the site scale was found to severely limit the analysis. Differences in approach led to a regime shift in percolation from a few, high magnitude events to frequent, low magnitude events. At the watershed scale, the variable hydraulic conductivity-based approach reduced average annual percolation by 20-50 mm, directly impacting the water balance and subsequently biophysical predictions. For instance, annual denitrification increased by 14-24 kg/ha for the new approach. Overall, the study demonstrates the need for continued efforts to enhance soil water model representation for improving biophysical process simulations.

Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk and make responsive economic management decisions as well as future investments. The objective of this study is to analyze and evaluate the application of proximal remote sensing from unmanned aerial systems (UAS) to detect excess water stress in soybean and predict the potential reduction in yield due to this excess water stress. A high-throughput data processing pipeline is developed to analyze multispectral images captured at the early development stages (R4–R5) from a low-cost UAS over two radiation use efficiency experiments in West–Central Indiana, USA. Above-ground biomass is estimated remotely to assess the soybean development by considering soybean genotype classes (High Yielding, High Yielding under Drought, Diversity, all classes) and transferring estimated parameters to a replicate experiment. Digital terrain analysis using the Topographic Wetness Index (TWI) is used to objectively compare plots more susceptible to inundation with replicate plots less susceptible to inundation. The results of the study indicate that proximal remote sensing estimates above-ground biomass at the R4–R5 stage using adaptable and transferable methods, with a calculated percent bias between 0.8% and 14% and root mean square error between 72 g/m2 and 77 g/m2 across all genetic classes. The estimated biomass is sensitive to excess water stress with distinguishable differences identified between the R4 and R5 development stages; this translates into a reduction in the percent of expected yield corresponding with observations of in-field flooding and high TWI. This study demonstrates transferable methods to estimate yield loss due to excess water stress at the plot level and increased potential to provide crop status assessments to stakeholders prior to harvest using low-cost UAS and a high-throughput data processing pipeline.

In this study, methods were developed to create and evaluate the performance of the Soil and Water Assessment Tool (SWAT) in southern Peru where commonly used input data sources were not available. Soil classes were defined based on regional soil taxonomy and suitability maps combined with soil profiles. Local land cover and remotely sensed satellite data were used to develop a land cover database. Water balance analysis of the reservoir as well as satellite evapotranspiration data were used for model performance assessment. Results showed that these strategies provided reliable predictions of hydrology in this region, with the uncertainty quantified based on the range of inputs. Overall, this semiarid watershed was base flow driven and average annual surface runoff contribution to streamflow was less than 9%. Assessment of water pathways and their uncertainties based on the uncertainty of estimated inputs also showed that 62% of precipitation was removed by evapotranspiration with up to 16% uncertainty. The methods introduced in this study can be applied to other data-scarce watersheds, and findings provide insights on the hydrology of the Peruvian Andes region.