Daily Time Step Research Articles

Applying a time-varying GEV distribution to correct bias in rainfall quantiles derived from regional climate models

Abstract Climate warming is causing an increase in extreme hydrometeorological events in most parts of the world. This phenomenon is expected to continue and will affect the frequency and intensity of extreme precipitation events. Although bias correction in regional climate model simulations has also been used to assess changes in precipitation extremes at daily and longer time steps, trends in the series predicted have seldom been considered. We present a novel bias correction technique that allows for the correcting of biases in the upper tails of the Generalized Extreme Value (GEV) distribution, while preserving the trend in projected precipitation extremes. The concept of non-stationary bias correction is demonstrated in a case study in which we used four EURO-CORDEX RCM models to estimate future rainfall quantiles. Historical observations have been used to correct biases in historical runs of the RCMs. The mean relative change in rainfall quantiles between the 1991–2021 historical period and the time horizon of 2080 was found to be 13.5% (st. dev.: 2.9%) for the return period of 2 years, which tends to decline with increasing return periods. Upon the return periods of 50 and 100 years, the mean relative change was predicted to be 5.5% (st. dev.: 1.1%) and 4.8% (st. dev.: 1%), respectively.

Generalised drought index: a novel multi-scale daily approach for drought assessment

Abstract. Drought is a complex climatic phenomenon characterised by water scarcity and is recognised as the most widespread and insidious natural hazard, posing significant challenges to ecosystems and human society. In this study, we propose a new daily based index for characterising droughts, which involves standardising precipitation and/or precipitation minus potential evapotranspiration (PET) data. The new index proposed here, the generalised drought index (GDI), is computed for the entire period available from the Iberian Gridded Dataset (1971 to 2015). Comparative assessments are conducted against the daily Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI), and a simple Z-Score standardisation of climatic variables. Seven different accumulation periods are considered (7, 15, 30, 90, 180, 360, and 720 d) with three drought levels: moderate, severe, and extreme. The evaluation focuses mainly on the direct comparison amongst indices in terms of their ability to conform to the standard normal distribution, added value assessment using the distribution added value (DAV), and a simple bias difference for drought characteristics. Results reveal that the GDI, together with the SPI and SPEI, follows the standard normal distribution. In contrast, the Z-Score index depends on the original distribution of the data. The daily time step of all indices allows the characterisation of flash droughts, with the GDI demonstrating added value when compared to the SPI and SPEI for the shorter and longer accumulations, with a positive DAV up to 35 %. Compared to the Z-Score, the GDI shows expected greater gains, particularly at lower accumulation periods, with the DAV reaching 100 %. Furthermore, the spatial extent of drought for the 2004–2005 event is assessed. All three indices generally provide similar representations, except for the Z-Score, which exhibits limitations in capturing extreme drought events at lower accumulation periods. Overall, the findings suggest that the new index offers improved performance and comparatively adds value to similar indices with a daily time step.

Monitoring Crop Condition at Gield Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR)

Operations that rely on optically-based Normalized Difference Vegetation Index (NDVI) to track crop productivity, are negatively impacted by cloud cover. In this research, parameters from Synthetic Aperture Radar (SAR) imagery were calibrated to NDVI with the targeted outcome to integrate SAR into Canadian crop monitoring services. Data from fully polarimetric RADARSAT-2 and dual-polarization Sentinel-1B imagery were calibrated to Sentinel-2 NDVI. Random Forest Regression (RFR) and Least Squares Boosting (LSBoost) were tested to calibrate SAR to NDVI (SARcal-NDVI) for six crops (corn, canola, soybeans, wheat, oats and barley). RADARSAT-2 provided the best fit with R2 exceeding 0.84 for canola, 0.87 for wheat and 0.90 for corn and soybeans. Correlations were lower for oats (R2 of 0.72–0.77) and barley (R2 of 0.43–0.64) due to a limited number of fields. Although a global model yielded lower correlations (R2 of 0.80), this model would be easier to implement into operations. Total power, the first and second eigenvalues and VH backscatter were important in reducing model error. The SARcal-NDVI and Growing Degree Days were then integrated into a Crop Structure Dynamic Model to produce daily estimates of crop condition, at field scales. The next step is to test if these daily estimates of crop condition can improve crop yield forecasting for the Canadian agriculture sector.

Internet-based surveillance to track trends in seasonal allergies across the United States.

Over a quarter of adults in the United States suffer from seasonal allergies, yet the broader spatiotemporal patterns in seasonal allergy trends remain poorly resolved. This knowledge gap persists due to difficulties in quantifying allergies as symptoms are seldom severe enough to warrant hospital visits. We show that we can use machine learning to extract relevant data from Twitter posts and Google searches to examine population-level trends in seasonal allergies at high spatial and temporal resolution, validating the approach against hospital record data obtained from selected counties in California, United States. After showing that internet-derived data can be used as a proxy for aeroallergen exposures, we demonstrate the utility of our approach by mapping seasonal allergy-related online activity across the 144 most populous US counties at daily time steps over an 8-year period, highlighting the spatial and temporal dynamics in allergy trends across the continental United States.

Multi-model hydrological reference dataset over continental Europe and an African basin

Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively validated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.

190 Implementation of an updated beef energetics and body composition model in the Crop Livestock Enterprise Model (CLEM)

Abstract The Crop Livestock Enterprise Model (CLEM) is a whole-farm bioeconomic model designed to simulate and explore ruminant performance across a wide range of production systems and management strategies. The model features a flexible, component-based structure that allows users to simulate multiple types of livestock production within the same system. Animals in the model are represented on an individual basis, allowing users to explore the effects of between-animal variation on a wide range of performance metrics at the animal and farm system levels. Recently, two new features were introduced to improve ability of CLEM to represent ruminant production systems: a user-defined timestep, and a new energetics model (based on the Australian ruminant feeding standards) that adds the ability to model changes in body composition in addition to existing predictions of body weight gain and feed intake. The change in timestep from monthly to daily was required to implement the new energetics model and allows for greater ability to compare CLEM results against field data, but other timesteps (e.g., weekly) can also be used depending on user preference. The Australian ruminant feeding standards were integrated with the existing CLEM modelling framework to create a revised energetics model for CLEM capable of predicting body composition, with additional updates to prediction of relative feed intake and the effect of diet type on feed quality. The energetics model was then evaluated for prediction of feed intake (DMI), final empty body weight (EBW), and fat and protein gain (Table 1) in feedlot cattle. The evaluation dataset consisted of treatment means (n = 29) from studies of Angus or British cross steers whose body composition (fat and protein) had been measured by serial slaughter. The updated model performed well for predicting final EBW, underpredicting by 2.35 kg on average (0.55% of the observed mean). Dry matter intake was underpredicted for 80% of treatments, with mean bias averaging 1.7 kg/d across all treatments and a root mean square prediction error (RMSPE, % of the observed mean) of 25%. Data on fat and protein gain was more variable than for EBW, and the model overpredicted protein gain and underpredicted fat gain in general, with mean bias of -51 g/d (RMSPE 42%) for protein and 270 g/d (RMSPE 58%) for fat. The addition of a daily timestep and a revised energetics model add to utility of CLEM by allowing for more in-depth exploration of the effects of feeding and management over time on intake, body weight gain, and body composition. The current version of CLEM can be downloaded from the APSIM Initiative website; and an upcoming update with additional options for prediction of intake and performance for both feedlot and grazing cattle will be made available.

Estimating high-resolution soil moisture by combining data from a sparse network of soil moisture sensors and remotely sensed MODIS LST information

ABSTRACT The present work demonstrates a methodology for acquiring high-resolution soil moisture information, namely at 1 km at a daily time step, utilizing data from a sparse network of soil moisture sensors and remotely sensed Land Surface Temperature (LST). Building on previous research and highlighting the strong correlation between surface soil moisture and LST, as a result of the thermal inertia, we first evaluated the correlation between Moderate Resolution Imaging Spectroradiometer (MODIS) LST and ground-based soil moisture information from soil moisture sensors installed in a pilot area in Northeastern Greece. Second, a regression formula was developed for three out of six soil moisture sensors, keeping the three remaining monitoring stations serving as a validation set. Furthermore, regression coefficients were interpolated at 1 km and the regression equations were applied for the entire study area, thus acquiring soil moisture information at a spatial resolution of 1 km at the daily time step. The verification process indicated a reasonable accuracy, with a mean absolute error (MAE) of <0.02 m3/m3. The results were considerably better than using a simple interpolation or downscaled daily 1-km SMAP soil moisture.

Leaf gas exchange, water use, and yield of Marula (Sclerocarya birrea) and Kei Apple (Dovyalis caffra): South African indigenous fruit trees with domestication potential

Conventional crop production is expected to decline in the future in sub-Saharan Africa due to the rising costs of inputs, soil degradation, and increasing water scarcity related to climate variability and change. South Africa has more than 30 species of indigenous fruit trees that are adapted to grow under harsh conditions, yet these species are currently underutilized. Their cultivation is constrained by the lack of detailed ecophysiological information required to develop effective management strategies that optimize tree performance. In this study we investigated how two species with domestication potential namely Marula (Sclerocarya birrea) and Kei apple (Dovyalis caffra) responded to climate and soil factors over a period of two years (2019–2021). Photosynthesis, transpiration, and yield were quantified for mature trees growing in experimental orchards in the Mpumalanga Province of South Africa. Leaf gas exchange data were collected at selected intervals during the growing season using an infrared gas analyser while whole tree transpiration rates were quantified using the heat ratio method of monitoring sap flow. These data were used to parameterize a big leaf Penman-Monteith (PM) model to estimate the transpiration dynamics of individual trees at the daily time step. S. birrea's transpiration responded strongly to both climatic and soil water content variations while the water use of D. caffra was driven mostly by climatic factors. Transpiration rates averaged 0.30 L/m2 of leaf area for S. birrea while that of D. caffra was around 0.45 L/m2. Both species exhibited significant alternate bearing with yield of individual trees fluctuating by more than 50 % between successive years. For the 2019/20 season, water productivity (grams of fruit per litre of water transpired) varied between 6.2 and 17.4 g/L for S. birrea and between 24.3 and 41.7 g/L for D. caffra. Whole tree transpiration by both species were accurately predicted using the PM model albeit with different parameters for each species. This study demonstrates that a simple PM model can accurately estimate the water requirements of these species in other growing regions.

Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach

Sandy soils are susceptible to excessive nitrogen (N) leaching under intensive crop production which is linked with the soil’s low nutrient holding capacity and high-water infiltration rate. Estimating soil mineral nitrogen (SMN) at the daily time-step is crucial in providing fertilizer recommendations balancing plant nitrogen use efficiency (NUE) and N losses to the environment. Crop models [e.g., Decision Support System for Agrotechnology Transfer (DSSAT)] can simulate SMN trend under varied fertilizer application rates and timings but struggle with accuracy in high-water table conditioned sub-irrigated soil. Alternatively, time-series deep learning (DL) models based on a long short-term memory (LSTM) are promising in understanding nonlinearity among complex variables. Yet, purely data-driven DL models for crops are challenging to develop due to limited field data availability and the excessive costs to collect more field data. Hence, a hybrid model (hybrid-LSTM) was developed by leveraging both the DSSAT and LSTM models to estimate daily SMN primarily using daily weather, applied fertilizer rates-timings, and the SMN sparse observations. This study used the observations from field trials conducted between 2011–2014 in Hastings, FL. The first step was to calibrate the DSSAT-SUBSTOR-Potato model to produce reliable SMN of the topsoil for treatments with varied N applied fertilizer rates split among the pre-planting, emergence, and tuber-initiation stages of the potato. Thereafter, the hybrid-LSTM model was trained on the calibrated DSSAT simulated SMN time-series and fine-tuned its predictions using the observed SMN to improve DSSAT simulated SMN. The hybrid-LSTM model was then tested on both calibrated and uncalibrated DSSAT SMN simulations where it outperformed the DSSAT model (improvement ranged ∼18–30 % on comparing the normalized root mean squared error) in providing reliable estimates of SMN across most of the farms and years. This novel hybrid modeling approach could guide stakeholders and farmers to build sustainable N management with improved crop NUE and yield and help in minimizing environmental losses.

Assessment of a downscaling using quantiles mapping with MIROC Model in Guadalquivir Basin, Bolivia

In this study historical and combined GS precipitation products were assessed versus climate change scenarios at Guadalquivir basin. The purpose of this study was to evaluate the downscaling procedure using Quantiles Downscaling Mapping (QDM). This scheme is based in statistical analysis where precipitation and temperature are adjusted using observed datasets. Moreover, simulated river discharge was obtained using HYDROBID tool. It was identified underestimation of combined GS precipitation product as well as simulated river discharge. Three climate change scenarios from MIROC CORDEX were used namely: RCP2.6, RCP4.5 and RCP8.5. The precipitation and temperature analyses were performed at daily time step. The results of QDM application showed more efficiency with RCP 8.5 and combined product GS for the 230 subbasins. About hydrological modeling using CORDEX variables, it was observed perturbed climate change using combined product GS with lower values against rain gauges. In this sense, precipitation and discharge were underestimated. Temperature showed the best performance at all cases. Finally, discharge series were integrated into volumes, where a decrease in water availability at the basins was noted as for near and far future. It is recommended to use perturbed scenarios with combined precipitation product since it is the most critical and takes into account the spatial variability with 230 syntactical rain gauges.

Water Tales from Turkistan: Challenges and Opportunities for the Badam-Sayram Water System under a Changing Climate

The Badam River, a tributary to the Arys River located in the Syr Darya basin, is a crucial natural resource for ecological, social, and economic activities in the semi-arid region of southern Kazakhstan. The river basin is heavily influenced by manmade water infrastructure and faces water scarcity, particularly during summer, highlighting the importance of understanding its hydrological processes for effective water resource management. In this study, a semi-distributed conceptual hydrological model of the Badam River was implemented using the RS MINERVE hydrological software to evaluate the impacts of climate change on hydrology and to test the resilience of the water system. Connected HBV models were implemented for each of the hydrological response units that were defined as altitudinal zones. The hydrological model was calibrated using daily time steps between 1979 and 2011, and the resulting flow exceedance curves and hydrographs were used to assess the potential impacts of climate change on the basin, using CMIP6 precipitation and temperature scenarios. Future climate scenarios for the 2054 – 2064 period demonstrate that the peak discharge will be shifted to spring/late spring compared to the current early summer with no significant decrease in average discharge per day of the year. The insights gained from this hydrological-hydraulic model can be used to effectively manage the water system and inform future hydropower design decisions and serve as a blueprint for similar studies in the region and elsewhere.

The economic effect of cow-based reproductive management programs with a systematic use of reproductive hormones

Hormone-based reproductive management programs can be beneficial to improve dairy cow's reproductive performance. This study aimed to compare the economic impact of reproductive management programs using systematic hormonal treatments to individual cows with a specific DIM range with a reproductive management program using cow-specific hormonal treatment based on a veterinary diagnosis of ovarian dysfunction during a fertility check. An existing individual cow-based, dynamic, and stochastic bio-economic simulation model, mimicking the production dynamics of a 200 cow-herd in daily time steps, was extended with ovarian dysfunction and fertility inputs. Four hormone-based reproductive management programs were modeled. In the default reproductive management program, reflecting the current reproductive management of Dutch herds, lactating dairy cows are inseminated based on detection of estrus and non-cyclic dairy cows are treated with hormones based on a veterinary diagnosis of ovarian dysfunction during a fertility check. Hormone treatments prescribed by the veterinarian for anestrus, cystic, and sub-estrus cows were an 8-d progesterone-releasing intravaginal device (PRID)-Synch protocol (PRIDsynch), an Ovsynch protocol, and a PGF2⍺ treatment, respectively. The 3 other reproductive management programs reflected systematic hormonal treatments to cows at specific DIM and included a 1) Double-Ovsynch protocol for TAI with nonpregnant cows submitted to a resynchronization protocol (FTAI), 2) Double-Ovsynch protocol for TAI with nonpregnant cows detected in estrus or submitted to a resynchronization protocol (FTAI+ED), and 3) detection of estrus with cows not detected submitted to a PRIDsynch protocol (ED+TAI). All nonpregnant cows were submitted to a resynchronization protocol based on the absence (PRIDsynch) or presence (Ovsynch protocol) of a corpus luteum (CL). The annual mean net economic return (NER) was calculated for all reproductive management programs. Compared with the default reproductive management program, the highest NER was observed for the FTAI+ED reproductive management program with €23,764 higher net revenues, followed by the FTAI and the ED+TAI reproductive management programs with €19,550 and €14,314 higher net revenues, respectively. Overall, systematic hormone-based reproductive management programs gave higher costs due to more hormones administered and higher calving and feed costs due to more pregnant cows. Nevertheless, the additional revenues of milk and calves in the systematic hormone-based reproductive management programs outweighed the total cost. For instance, the FTAI+ED reproductive management program gave €8,953 higher total cost per year compared with the default but with €32,654 higher revenues. In summary, reproductive management programs where hormones were systematically used gave economic advantages over the current default reproductive management program in which hormones are administered to individual cows based on a veterinary diagnosis of ovarian dysfunction during a fertility check.

Climate warming impacts on ringed seal breeding habitat in Svalbard

Global warming is occurring at an accelerated rate in the Arctic compared to other parts of the planet with sea-ice declines being among the most striking manifestations of Arctic climate-related changes. Impacts of ongoing Arctic environmental change have been documented for biota throughout marine ecosystems from protists to top predators. Ice-dependent species with specific habitat needs are particularly vulnerable to the ongoing changes. The ringed seal (Pusa hispida) is an ice-associated Arctic endemic species that gives birth and rests in snow caves built in drifts of snow over holes in the sea ice created and maintained by these seals. In this study we create a snow-on-sea-ice reproductive lair habitat model for ringed seals in the Svalbard Archipelago (Norway), a hot-spot of Arctic warming. We use SnowModel, a physics-based snow distribution and evolution simulation system, as the core for a lair habitat model. The model quantifies snow depth and blowing snow fluxes and also relates these variables to snow availability for seal lair habitat. This was accomplished by developing an ecologically informed snow variable that quantifies potential seal lair habitat availability as a function of blowing snow fluxes. Model simulations were performed for the period September 1987 – August 2021 (34 years) on a 500 m × 500 m grid using a daily time-step. Field observations of snow depth and gridded analyses of sea-ice concentration and near-surface (+10 m) atmospheric forcing (air temperature, relative humidity, precipitation, and wind speed and direction) were incorporated within the model simulations. The results show that both snow depth and potential seal lair habitat have been decreasing in Svalbard for the last two decades. If current trends continue, as expected, ringed seal lair habitat will cease to exist across much of the Svalbard Archipelago in the next decade, putting this important Arctic species at risk of regional extirpation.

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.

Impacts on water quality of producing biogas on pig farms as a function of the associated agricultural practices

The aim of this study was to assess positive or negative impacts of anaerobic digestion (AD) on water quality using a systemic approach. To this end, we used the agro-hydrological model Topography-based Nitrogen Transfer and Transformation (TNT2), a spatially explicit model that simulates nitrogen and water flows at the watershed scale on a daily time step. Four scenarios were constructed and analyzed: a baseline before the introduction of AD (S0), AD with adjusted fertilization (S1), AD with unadjusted fertilization (S2), and agroecological AD (S3). The results showed that, when spreading practices were similar and an equivalent amount of effective nitrogen was applied, digested pig slurry generally had a predicted amount of nitrate leaching similar to that of undigested pig slurry. In addition, replacing catch crops with energy cover crops had little impact on water quality. Scenario S3 was the most favorable one for water quality and biogas production, but not for soil organic nitrogen storage and food and feed production. This study’s strength is its systemic approach, which considered both environmental and agronomic aspects to assess the scenarios.

Sensitivity of the stock assessment for the Antarctic krill fishery to time‐varying natural and fishing mortality

AbstractThe stock assessment model for the Antarctic krill fishery is a population model operating on daily timesteps, which permits modeling within‐year patterns of some population dynamics. We explored the effects of including within‐year patterns in natural and fishing mortality on catch limits of krill, by incorporating temporal presence of key predator species and contemporary temporal trends of the fishing fleet. We found that inclusion of within‐year variation in natural and fishing mortalities increased catch limits. Fishing mortality had a greater effect than natural mortality despite differences in top‐down predation on krill, and potentially increased catch limits by 24% compared to the baseline model. Additionally, the stock assessment model allowed a higher catch limit when fishing was during peak summer months than autumn. Number of days with active fishing was negatively related to precautionary catch limits. Future stock assessments should incorporate contemporary spatiotemporal fishing trends and consider implementing additional ecosystem components into the model.

Predicting & Analysing Discharge and Sediment of Sore Catchment, Baro-Akobo Sub Basin Using Saturation Excess Model, Western Ethiopia

Soil erosion, land degradation, and soil loss are major problems in Ethiopia's Baro-Akobo Basin. In the Ethiopian highlands, many hydrological models have been employed to forecast sediment and discharge in watersheds of varying sizes. PED mode was used for this study because it requires fewer data and is well recognized by academics in Ethiopia's Upper Blue Nile. The parameter efficient semi-distributed watershed model (PED) was used to study the prediction of discharge and sediment in the Sore watershed. The objective of this study was to analyze and forecast the sediment and discharge from the Sore catchment in the western Ethiopian region of the Baro-Akobo Sub basin. The selected watershed was Sore (1665.5 Km2) in the Baro-Akobo River Basin. The stream flow and sediment data for the Sore watershed from 2005 to 2013 and 2014 to 2018 were used for the model's calibration and validation by the Ministry of Water and Electricity. For the Sore watershed, the daily time step scale model efficiency of PED-W was found to be NSE= 0.40 and NSE= 0.44 for the calibration of sediment and discharge, respectively. Similarly, NSE values for the Sore watershed's NSE= -0.82 and PED-W's discharge and sediment during the validation period were found. As a result, there was a range of uneven to acceptable agreement between the simulated and observed discharge and sediment at daily time steps. In comparison to the daily time step scale, the models' performance varied and performed better on the monthly time step scale. According to the overall model performance, the PED-W model was shown to be less accurate than the SWAT model for estimating stream flow and sediment output. The PED model, on the other hand, was exactly the same as the previously employed SWAT model and was used for calibration and validation at monthly time steps.

Using Object-Oriented Simulation to Assess the Impact of the Frequency and Accuracy of Mobility Scoring on the Estimation of Epidemiological Parameters for Lameness in Dairy Herds.

Mobility scoring data can be used to estimate the prevalence, incidence, and duration of lameness in dairy herds. Mobility scoring is often performed infrequently with variable sensitivity, but how this impacts the estimation of lameness parameters is largely unknown. We developed a simulation model to investigate the impact of the frequency and accuracy of mobility scoring on the estimation of lameness parameters for different herd scenarios. Herds with a varying prevalence (10, 30, or 50%) and duration (distributed around median days 18, 36, 54, 72, or 108) of lameness were simulated at daily time steps for five years. The lameness parameters investigated were prevalence, duration, new case rate, time to first lameness, and probability of remaining sound in the first year. True parameters were calculated from daily data and compared to those calculated when replicating different frequencies (weekly, two-weekly, monthly, quarterly), sensitivities (60-100%), and specificities (95-100%) of mobility scoring. Our results showed that over-estimation of incidence and under-estimation of duration can occur when the sensitivity and specificity of mobility scoring are <100%. This effect increases with more frequent scoring. Lameness prevalence was the only parameter that could be estimated with reasonable accuracy when simulating quarterly mobility scoring. These findings can help inform mobility scoring practices and the interpretation of mobility scoring data.

Spatio‐Temporal Machine Learning for Regional to Continental Scale Terrestrial Hydrology

AbstractIntegrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow‐CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event‐scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process‐based models without sacrificing fidelity.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.