Water Balance Research Articles

Simulating the Thermal Regime and Surface Energy Balance of a Permafrost‐Underlain Forest in Mongolia

AbstractForests overlap with large parts of the northern hemisphere permafrost area, and representing canopy processes is therefore crucial for simulating thermal and hydrological conditions in these regions. Forests impact permafrost through the modulation of radiative fluxes and exchange of turbulent fluxes, precipitation interception and regulation of transpiration. Forests also feature distinct soil layers of litter and organic matter, which play central roles for the infiltration and evaporation of water, while also providing thermal insulation for deeper ground layers. In this study, we present a new module within the CryoGrid community model to simulate forest ecosystems and their impact on the surface water and energy balance. The module includes a big‐leaf vegetation scheme with adaptations for canopy heat storage and transpiration. Furthermore, we account for the effect of surface litter layers on water and energy transfer. We show that the model is capable of simulating radiation, snow cover and ground temperatures below a deciduous needleleaf forest on a north‐facing slope in the Khentii Mountains in Central Mongolia. A sensitivity analysis of topographic aspect and ecosystem configuration confirms the important role of the litter layers for the energy and water balance of the ground. Furthermore, it suggests that the presence of permafrost is primarily linked to topographic aspect rather than the presence of forest at this site. The presented model scheme can be used to study the development of the ground thermal regime in forests, including the state of permafrost, under different climate, ecosystem, and land use scenarios.

Cannabinoid receptor type 1 activation causes a water diuresis by inducing an acute central diabetes insipidus in mice.

Cannabis and synthetic cannabinoid consumption are increasing worldwide. Cannabis contains numerous phytocannabinoids that act on the G protein-coupled cannabinoid receptor type 1 (CB1R) and cannabinoid receptor type 2 expressed throughout the body, including the kidney. Essentially every organ, including the kidney, produces endocannabinoids, which are endogenous ligands to these receptors. Cannabinoids acutely increase urine output in rodents and humans, thus potentially influencing total body water and electrolyte homeostasis. As the kidney collecting duct (CD) regulates total body water, acid/base, and electrolyte balance through specific functions of principal cells (PCs) and intercalated cells (ICs), we examined the cell-specific immunolocalization of CB1R in the mouse CD. Antibodies against either the C-terminus or N-terminus of CB1R consistently labeled aquaporin 2 (AQP2)-negative cells in the cortical and medullary CD and thus presumably ICs. Given the well-established role of ICs in urinary acidification, we used a clearance approach in mice that were acid loaded with 280 mM NH4Cl for 7 days and nonacid-loaded mice treated with the cannabinoid receptor agonist WIN55,212-2 (WIN) or a vehicle control. Although WIN had no effect on urinary acidification, these WIN-treated mice had less apical + subapical AQP2 expression in PCs compared with controls and developed acute diabetes insipidus associated with the excretion of large volumes of dilute urine. Mice maximally concentrated their urine when WIN and 1-desamino-8-d-arginine vasopressin [desmopressin (DDAVP)] were coadministered, consistent with central rather than nephrogenic diabetes insipidus. Although ICs express CB1R, the physiological role of CB1R in this cell type remains to be determined.NEW & NOTEWORTHY The CB1R agonist WIN55,212-2 induces central diabetes insipidus in mice. This research integrates existing knowledge regarding the diuretic effects of cannabinoids and the influence of CB1R on vasopressin secretion while adding new mechanistic insights about total body water homeostasis. Our findings provide a deeper understanding about the potential clinical impact of cannabinoids on human physiology and may help identify targets for novel therapeutics to treat water and electrolyte disorders such as hyponatremia and volume overload.

Quantification of Water Balance of Terraced Paddy Field in Northern Bali by Using Remote Monitoring

Quantification of Water Balance of Terraced Paddy Field in Northern Bali by Using Remote Monitoring

Water use efficiency and water productivity of aerobic rice under drip irrigation and fertigation system by using daily soil water balance

Water use efficiency and water productivity of aerobic rice under drip irrigation and fertigation system by using daily soil water balance

THE IMPORTANCE OF NUTRITION AT THE STAGE OF BODY WEIGHT CORRECTION IN SPORTS

Correction of body weight is one of the significant factors on the way for athletes to achieve the much-desired success and victories. An individual approach to nutrition, correct water balance, additional administration of vitamins and microelements can make the process of body weight correction safer and more harmonious.

Remineralization of desalinated water: Duality roles of H2SO4 and CO2 injection during calco-carbonic equilibrium of osmosis water

In a desalination station, the remineralization step of osmosis water (OW) is essential to return to produced water its calco-carbonic equilibrium. For this, the use of calcite CaCO3 or lime Ca(OH)2 for water equilibrium in post-treatment processes needs the utilization of both CO2 and/or H2SO4 in osmosis water. For this reason, we describe here in detail, the effect of H2SO4 and CO2 on the remineralization process in a post-treatment desalination plant, especially with using hydrated lime Ca(OH)2 and calcite contactor (CaCO3)·In this paper, the different cases were discussed and investigated, by monitoring several indicator parameters such as pH, Ca2+ content, alkalinity, Langelier Index, etc. After the remineralization stage, we have shown that the efficiency of the remineralization process by CaCO3 or Ca(OH)2 depends on CO2 or H2SO4 content. Remineralization by CaCO3 (limestone) coupled with CO2 acidification is easy and more operator-friendly compared to the process using Ca(OH)2 (lime); it provides a clean environment for people working in the plant. In addition, the H2SO4 injection in the pretreatment stage followed by CaCO3 contact could lead to great results and correct calco-carbonic equilibrium. In the end, a comparative study of the investment cost in each process shows that the acidification of raw water with sulfuric acid (pretreatment) is the cheapest one, according to many studies. But on the other hand, the direct injection of CO2 is the easiest one to use for water balancing.

Catchment Coevolution and the Geomorphic Origins of Variable Source Area Hydrology

AbstractFeatures of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long‐term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro‐geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non‐dendritic, trellis‐like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro‐geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long‐term landscape evolution.

Analysis of terrestrial water storage variations in South Korea using GRACE satellite and GLDAS data in Google Earth Engine

ABSTRACT With the requirement of a macroscopic approach to understanding the relationship between water resources and hydrological phenomena, such as severe droughts under climate change, this study investigates South Korea’s long-term terrestrial water storage (TWS) using GRACE satellite data. A detailed analysis of water balance in TWS, based on the GLDAS model outputs in Google Earth Engine, reveals the following results: (1) TWS anomaly shows an average decrease of −33.5 mm year−1 from 2003 to 2016; (2) spatial shifts in TWS anomaly (−1.176 to −0.424 cm) unveil regional water storage dynamics, indicating negative temporal slope changes per grid cell (−0.393 to −0.225); (3) contributions of precipitation to TWS are not always straightforward, due to runoff inefficiencies affecting water storage and groundwater; (4) accessible water, integrating surface water and groundwater linked only to the shallow layer’s soil moisture, constrains deep groundwater accounting, emphasizing the need for local groundwater surveys in further research.

Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction

Hydrological forecasting is of great importance for water resources management and planning, especially given the increasing occurrence of extreme events such as floods and droughts. The physics-informed machine learning (PIML) models effectively integrate conceptual hydrologic models with machine learning (ML) models. In this process, the intermediate variables of PIML models serve as bridges between inputs and outputs, while the impact of intermediate variables on the performance of PIML models remains unclear. To fill this knowledge gap, this study aims to encompass the construction of PIML models based on various hydrologic models, conduct comparative analyses of different intermediate variables based on a case study of 205 CAMELS basins, and further explore the relationship between the performance of PIML models and catchment characteristics. The optimal ML model for constructing PIML is first selected among four ML models within the 205 basins. The PIML models are then developed based on five monthly water balance models, namely TM, XM, MEP, SLM, and TVGM. To quantify the potential impact of difference in intermediate variables, two sets of experiments are further designed and performed, namely S1 with actual evapotranspiration as the intermediate variable and S2 with soil moisture as the intermediate variable. Results show that five PIML models generally outperformed the optimal standalone ML models, i.e., the Lasso model. Specifically, regardless of the choice of intermediate variables, the PIML-XM model consistently outperformed the other models within the same basins. Almost all constructed PIML models are affected by the intermediate variables in monthly runoff simulations. Typically, S1 exhibited better performance compared to S2. A greater impact of aridity index, forest fraction, and catchment area on model performance is observed in S2. These findings improve our understanding of constructing PIML models in hydrology by emphasizing their excellent performance in runoff simulations and highlighting the importance of intermediate variables.

Alignment between water inputs and vegetation green‐up reduces next year's runoff efficiency

AbstractIn the western United States, water supplies largely originate as snowmelt from forested land. Forests impact the water balance of these headwater streams, yet most predictive runoff models do not explicitly account for changing snow‐vegetation dynamics. Here, we present a case study showing how warmer temperatures and changing forests in the Henrys Fork of the Snake River, a seasonally snow‐covered headwater basin in the Greater Yellowstone Ecosystem, have altered the relationship between April 1st snow water equivalent (SWE) and summer streamflow. Since the onset and recovery of severe drought in the early 2000s, predictive models based on pre‐drought relationships over‐predict summer runoff in all three headwater tributaries of the Henrys Fork, despite minimal changes in precipitation or snow accumulation. Compared with the pre‐drought period, late springs and summers (May–September) are warmer and vegetation is greener with denser forests due to recovery from multiple historical disturbances. Shifts in the alignment of snowmelt and energy availability due to warmer temperatures may reduce runoff efficiency by changing the amount of precipitation that goes to evapotranspiration versus runoff and recharge. To quantify the alignment between snowmelt and energy on a timeframe needed for predictive models, we propose a new metric, the Vegetation‐Water Alignment Index (VWA), to characterize the synchrony of vegetation greenness and snowmelt and rain inputs. New predictive models show that in addition to April 1st SWE, the previous year's VWA and summer reference evapotranspiration are the most significant predictors of runoff in each watershed and provide more predictive power than traditionally used metrics. These results suggest that the timing of snowmelt relative to the start of the growing season affects not only annual partitioning of streamflow, but can also determine the groundwater storage state that dictates runoff efficiency the following spring.

Comparison of different machine-learning algorithms for land use land cover mapping in a heterogenous landscape over the Eastern Nile river basin, Ethiopia

Land use/land cover (LULC) information is regarded as one of the most important variables in global change studies. Several LULC classification algorithms are available with different levels of accuracy in LULC mapping under different geographical setups. Furthermore, different remote sensing (RS) based indices and auxiliary data have been reported to have contributed to the accuracy of LULC mapping. Hence, the main aim of this study was to compare the different ML algorithms for the LULC mapping and explicitly assess the potential contribution of different RS-derived indices and auxiliary data on classification accuracy in the Upper Tekeze-Atbarah (UTA) river basin in Ethiopia. The overall classifiers evaluation result revealed that random forest (RF) performed best and followed by K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), and Classification and Regression Tree (CART). Compared to RF, the MLC method showed less accuracy by 16.2% (19.7%) in OA (kappa coefficient) respectively, which showed that the currently emerging ML classifiers contributed to an improvement in LULC mapping. The use of RS-derived indices and DEM as predictors together with the Landsat bands improved the overall accuracy and kappa coefficient by 5.5% and 7% respectively. Furthermore, the discrepancy in the classified LULC map from RF and other classifiers was compared at pixel-level and the result showed a considerable disagreement between the LULC maps. For instance, when the LULC map from RF and KNN are compared, only 80.6% of the area has the same LULC condition. This result reflects the potential discrepancy that could exist in LULC maps from different classifiers. The hydrological simulation under each of the classified map also resulted in a considerable difference in the major water balance components. In a conclusion, given the considerable difference in classifier performance and the potential propagation of errors in LULC maps to further applications such as water resources assessment, it is highly recommended to explicitly evaluate the available classifiers. Users should also harness the power of RS indices and auxiliary datasets for improved LULC mapping.

Synergistic impact of climate and land use land cover change dynamics on the hydrological regime of Loktak Lake catchment under CMIP6 scenarios

Study region: Loktak Lake catchment, north-eastern Himalayan ranges, IndiaStudy focus: Assessing the potential synergistic impact of land use land cover (LULC) and climate change on water resources is crucial for watershed management especially in the data-scarce vulnerable wetlands. Using the SWAT hydrological model, the current study seeks to quantify the combined impacts of LULC and climate change on the water balance parameters of the Loktak Lake catchment. This study simulates the hydrological regime of the Loktak Lake catchment based on future climate and changes to LULC dynamics using four GCM models in combination with SWAT models under two alternative scenarios.New hydrological insights for the region: The findings showed that under the SSP 126 and SSP 585 scenarios, the annual mean temperature is anticipated to increase by a maximum of 1.7°C and 3.74°C, respectively, by the 2050 s and 2090 s, while the annual precipitation similarly shown a rising trend for both the scenarios for the mid and far future. The mean annual streamflow is projected to increase by 48.84 cumecs (31.65 %) and 57 cumecs (36.89 %) by the 2050 s and 2090 s decades, respectively, while the water yield will rise by 200.65 mm (30.25 %) and 216.1 mm (32.57 %) in comparable decades. The outcomes of this study might provide a scientific foundation for ecological protection as well as water resource management and development in response to the potential future risk due to climate change in the Loktak Lake catchment.

Ecohydrological assessment of the water balance of the world's highest elevation tropical forest (Polylepis)

Polylepis trees grow at elevations above the continuous tree line (3000–5000 m a.s.l.) across the Andes. They tolerate extreme environmental conditions, making them sensitive bioindicators of global climate change. Therefore, investigating their ecohydrological role is key to understanding how the water cycle of Andean headwaters could be affected by predicted changes in environmental conditions, as well as ongoing Polylepis reforestation initiatives in the region. We estimate, for the first time, the annual water balance of a mature Polylepis forest (Polylepis reticulata) catchment (3780 m a.s.l.) located in the south Ecuadorian páramo using a unique set of field ecohydrological measurements including gross rainfall, throughfall, streamflow, and xylem sap flow in combination with the characterization of forest and soil features. We also compare the forest water balance with that of a tussock grass (Calamagrostis intermedia) catchment, the dominant páramo vegetation. Annual gross rainfall during the study period (April 2019–March 2020) was 1290.6 mm yr−1. Throughfall in the Polylepis forest represented 61.2 % of annual gross rainfall. Streamflow was the main component of the water balance of the forested site (59.6 %), while its change in soil water storage was negligible (<1 %). Forest evapotranspiration was 54.0 %, with evaporation from canopy interception (38.8 %) more than twice as high as transpiration (15.1 %). The error in the annual water balance of the Polylepis catchment was small (<15 %), providing confidence in the measurements and assumptions used to estimate its components. In comparison, streamflow and evapotranspiration at the grassland site accounted for 63.7 and 36.0 % of the water balance, respectively. Although evapotranspiration was larger in the forest catchment, its water yield was only marginally reduced (<4 %) in relation to the grassland catchment. The substantially higher soil organic matter content in the forest site (47.6 %) compared to the grassland site (31.8 %) suggests that even though Polylepis forests do not impair the hydrological function of high-Andean catchments, their presence contributes to carbon storage in the litter layer of the forest and the underlying soil. These findings provide key insights into the vegetation-water‑carbon nexus in high Andean ecosystems, which can serve as a basis for future ecohydrological studies and improved management of páramo natural resources considering changes in land use and global climate.

New insights into the parameterization of the dry surface layer and its hydrogeochemical mechanism: An experimental study

Knowledge of the parameterization of the dry surface layer (DSL) is essential for evaluating near-surface water flow and water balance in arid and semi-arid areas. Existing studies have parameterized DSL thickness and vapor flow as functions of the soil moisture content (SMC) in the surface layer to predict soil evaporation. However, hydrochemical processes related to DSL development have been ignored, including changes in hydrochemistry, the underlying hydrochemical mechanism, and the role of dissolved substances in the DSL development. Herein, we performed a series of soil evaporation experiments for 260 days and explored the factors influencing DSL development (e.g., soil texture, atmospheric temperature, SMC, solutes). Evaporation experiments were performed using silty loess, sandy loess, and fine sand with a 60-cm water table. Results showed that the cumulative evaporation of silty loess, sandy loess, and fine sand over the experimental period were 1,391.52, 460.10, and 185.53 mm, respectively, which determined by the maximum height of liquid flow continuity. The content of total dissolved solids (TDS) and major ions at the surface soil were significantly higher than the values at deep depths of 5‒55 cm, which largely depend on evaporative water loss. Evolutionary trends of chemical facies in sand media along the liquid water migration were from HCO3-Ca type to SO4·Cl-Na type. This was attributed to mineral dissolution at a depth of 5–55 cm and their transport with liquid water, resulting in the precipitation of salt crystals at the surface soil. Furthermore, a consolidated DSL with a thickness of 3.0–3.5 cm in the sandy loess and a loose DSL with a thickness of 1.5–2.0 cm in fine sand were observed at the end of the experiments. The accumulation of solutes at the surface leads to a reduction in effective porosity and the aggregation of soil particles during continuous drying, which facilitates the consolidation of DSL in sandy loess. This overestimated the DSL thickness, resulting in a difference between the experimental and predicted evaporation rates by Fick's law. Overall, these results highlight the limitations of considering DSL thickness as a function of SMC only, providing new insights into hydrochemical processes and dissolved solutes involving DSL parameterization during continuous soil drying.

A warmer climate impairs the growth performance of Central Europe's major timber species in lowland regions

Recent hot droughts have caused tree vitality decline and increased mortality in many forest regions on earth. Most of Central Europe's important timber species have suffered from the extreme 2018/2019 hot drought, confronting foresters with difficult questions about the choice of more drought- and heat-resistant tree species. We compared the growth dynamics of European beech, sessile oak, Scots pine and Douglas fir in a warmer and a cooler lowland region of Germany to explore the adaptive potential of the four species to climate warming (24 forest stands). The basal area increment (BAI) of the two conifers has declined since about 1990–2010 in both regions, and that of beech in the warmer region, while oak showed positive BAI trends. A 2 °C difference in mean temperatures and a higher frequency of hot days (temperature maximum >30 °C) resulted in greater sensitivity to a negative climatic water balance in beech and oak, and elevated sensitivity to summer heat in Douglas fir and pine. This suggests to include hot days in climate-growth analyses. Negative pointer years were closely related to dry years. Nevertheless, all species showed growth recovery within one to three years. We conclude that all four species are sensitive to a deteriorating climatic water balance and hot temperatures, and have so far not been able to successfully acclimate to the warmer climate, with especially Douglas and beech, but also Scots pine, being vulnerable to a warming and drying climate.

An Isotope Mass Balance Analysis of Evaporative Loss From Lake Turkana, Kenya Using δ18O and δD of Natural Waters

AbstractMeasurements of oxygen and hydrogen stable isotope ratios (δ18O and δD) in meteoric waters provide insight to overlapping effects of evaporation, precipitation, and mixing on basin scale hydrology. This study of waters collected between 2016 and 2021 in the Turkana Basin, northern Kenya, uses δ18O and δD to understand water balance in Lake Turkana, a large, low‐latitude, alkaline desert lake. The Omo River, a major river system in the Ethiopian Highlands, is historically understood to provide approximately 90% of the water input to Lake Turkana. Discharge of the Omo is prohibitively difficult to measure, but stable isotope ratios in the lake may provide a meaningful method for monitoring the lake's response to changes in input. Precipitation in the Turkana Basin is low (&lt;200 mm/year) with negligible rainfall on the lake's surface, and all water loss from the lake is evaporative. We compare new measurements with previous data from the region and records of lake height and precipitation from the same time period. We show that a Bayesian approach to modeling evaporation using atmospheric conditions and river δ18O and δD yields results consistent with published water balance models. Continued sampling of lake and meteoric waters in the Turkana Basin will be a useful way to monitor the lake's response to regional and global climate change.

Exposure to teflubenzuron reduces drought tolerance of collembolans

Chitin synthesis inhibitors (CSIs) are commonly used insecticides compromising cuticle formation and structure in arthropods. Arthropods rely on intact cuticles to maintain water balance and cellular homeostasis to survive in different weather conditions. We hypothesized that physiological impacts of CSIs may make arthropods more vulnerable to harsh environmental conditions, such as extreme heat, cold or drought. The aim of this study was to investigate if pre-exposure to teflubenzuron (a common CSI) would influence Folsomia candida's (Collembola: Isotomidae) sensitivity to natural stressors. Here, we exposed adult collembolans to teflubenzuron through food for two weeks, then survivors were immediately divided into three groups for subsequent acute heat, cold, and drought exposure. After acute exposure to these natural stressors, the collembolans were moved to optimal conditions for a one-week recovery period during which their survival, time to regain reproduction, and egg production were examined. We analyzed the interaction between effects of teflubenzuron and natural stressors using a multiplicative model. No interaction between effects of teflubenzuron and heat was observed in any test endpoints. A synergistic interaction between effects of teflubenzuron and cold was observed in the time to regain reproduction. Both survival and egg production, on the other hand, showed synergistic interaction between effects of teflubenzuron and drought, as well as a tendency for longer reproduction recovery times. Our results suggest that pre-exposure to teflubenzuron reduces drought tolerance in F. candida, while its impact on heat or cold tolerance is minor or absent. This study is among the first to explore the combined effects of CSI and natural stressors on soil arthropods, providing more insight on potential risks posed by such chemicals in the environment.

Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

A targeted CRISPR-Cas9 mediated F0 screen identifies genes involved in establishment of the enteric nervous system.

The vertebrate enteric nervous system (ENS) is a crucial network of enteric neurons and glia resident within the entire gastrointestinal tract (GI). Overseeing essential GI functions such as gut motility and water balance, the ENS serves as a pivotal bidirectional link in the gut-brain axis. During early development, the ENS is primarily derived from enteric neural crest cells (ENCCs). Disruptions to ENCC development, as seen in conditions like Hirschsprung disease (HSCR), lead to the absence of ENS in the GI, particularly in the colon. In this study, using zebrafish, we devised an in vivo F0 CRISPR-based screen employing a robust, rapid pipeline integrating single-cell RNA sequencing, CRISPR reverse genetics, and high-content imaging. Our findings unveil various genes, including those encoding opioid receptors, as possible regulators of ENS establishment. In addition, we present evidence that suggests opioid receptor involvement in the neurochemical coding of the larval ENS. In summary, our work presents a novel, efficient CRISPR screen targeting ENS development, facilitating the discovery of previously unknown genes, and increasing knowledge of nervous system construction.

Reframing acute kidney injury as a pathophysiological continuum of disrupted renal excretory function.

Surrogate measures of glomerular filtration rate (GFR) continue to serve as pivotal determinants of the incidence, severity, and management of acute kidney injury (AKI), as well as the primary reference point underpinning knowledge of its pathophysiology. However, several clinically important deficits in aspects of renal excretory function during AKI other than GFR decline, including acid-base regulation, electrolyte and water balance, and urinary concentrating capacity, can evade detection when diagnostic criteria are built around purely GFR-based assessments. The use of putative markers of tubular injury to detect "sub-clinical" AKI has been proposed to expand the definition and diagnostic criteria for AKI, but their diagnostic performance is curtailed by ambiguity with respect to their biological meaning and context specificity. Efforts to devise new holistic assessments of overall renal functional compromise in AKI would foster the capacity to better personalize patient care by replacing biomarker threshold-based diagnostic criteria with a shift to assessment of compromise along a pathophysiological continuum. The term AKI refers to a syndrome of sudden renal deterioration, the severity of which is classified by precise diagnostic criteria that have unquestionable utility in patient management as well as blatant limitations. Particularly, the absence of an explicit pathophysiological definition of AKI curtails further scientific development and clinical handling, entrapping the field within its present narrow GFR-based view. A refreshed approach based on a more holistic consideration of renal functional impairment in AKI as the basis for a new diagnostic concept that reaches beyond the boundaries imposed by the current GFR threshold-based classification of AKI, capturing broader aspects of pathogenesis, could enhance AKI prevention strategies and improve AKI patient outcome and prognosis.