Antecedent Precipitation Index Research Articles

Climate impacts on high-intensity sapling mortality: Comparative study and future warnings for Larix kaempferi, Abies sachalinensis, and Picea glehnii

Climate change profoundly affects plant viability and forest dynamics, creating uncertainty in future forest management. Post-planting, high-intensity sapling mortality may require costly replanting, disrupting plans, and risking forest management goals. Therefore, this study focused on high-intensity sapling mortality (mortality rates exceeding 30 %) in Larix kaempferi, Abies sachalinensis, and Picea glehnii planted in northern Japan attributed to summer drought. Utilizing a dataset spanning approximately three decades, the correlation between high-intensity mortality occurrence and climatic stress was explored employing a Bayesian approach. Additionally, the probability of high-intensity mortality occurrence by the end of this century was predicted based on two representative climate scenarios (RCP2.6 and RCP8.5). The predictive accuracy of high-intensity mortality occurrence probabilities exceeded 70 % after comprehensive consideration of climatic stress factors, thereby accentuating the robustness of the proposed model. These encompass the antecedent precipitation index (API), which serves as a soil drought indicator; 30-day mean daily maximum temperature (MaxTMP̅); 30-day mean daily total solar radiation (TSR̅); and day of year (DOY). All sapling species manifested a pronounced negative correlation between API and the probability of high-intensity mortality occurrence, whereas TSR̅ exhibited a positive impact on this probability. Additionally, MaxTMP̅ positively influenced this probability for L. kaempferi and A. sachalinensis. Delving into interspecific differentials, API exhibited the most pronounced impact on P. glehnii in comparison to the other two tree species, whereas TSR̅ exerted a substantial influence on A. sachalinensis and P. glehnii. The susceptibility to MaxTMP̅ was hierarchically ordered as L. kaempferi >A. sachalinensis >P. glehnii. DOY, used to assess phenological effects like leaf morphology, negatively affected the probability of high-intensity mortality occurrence exclusively for L. kaempferi. The median probability of high-intensity mortality occurrence in Hokkaido, calculated from the estimated probabilities across all regions of Hokkaido (encompassing non-forested areas but excluding remote islands), at a 1 km resolution, was 0.37 for L. kaempferi, 0.57 for A. sachalinensis, and 0.41 for P. glehnii under RCP2.6 by the century’s end. These probabilities have decreased in comparison to those observed in 2018–2022. In stark contrast, under RCP8.5, the probabilities soared to 0.91, 0.87, and 0.56, representing an increase of 2.12 times for L. kaempferi, 1.36 times for A. sachalinensis, and 1.14 times for P. glehnii in comparison to 2018–2022. Even the least affected species, P. glehnii, witnessed a increase in mortality probability. This study underscores the importance of forest management and adaptation to climate change.

Evaluating the impacts of flooding on crop yields by different meteorological indices: A regional case study in the middle-lower reach of the Yangtze River, China

Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts

Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from SM observations.

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Evaluating flash flood vulnerability using combined deterministic and parametric model: A case in the upper Ciliwung watershed, Indonesia

On January 19, 2021, a flash flood hit the Upper Ciliwung watershed, a mountainous area in Indonesia, causing damage to seven buildings and displacing 1,800 residents. To minimize the impact of such disasters, there is a need for disaster risk awareness and management specifically focused on flash floods in mountainous regions. Therefore, this study aims to assess the flash flood potential index value in the Upper Ciliwung watershed. The research utilized deterministic (Soil and Water Assessment Tool-SWAT) and parametric (Flash Flood Potential Index-FFPI) modeling to analyze various factors, including slope, geology, ground movement, Antecedent Precipitation Index (API), and Runoff Coefficient (Curve Number-CN). The models showed satisfactory goodness-of-fit statistics with R2 and Nash-Sutcliffe efficiencies (NSE) values of 0.58 and 0.57, respectively. The findings indicated that 39.95% of the watershed had a moderate vulnerability index value of 3, while downstream areas (31.7%) had a low vulnerability index value of 2, and the middle and upstream regions had high vulnerability index values of 4 and 5, covering 23.65% and 2.49% of the total area. These results provide valuable insights to local authorities for implementing measures to reduce the Upper Ciliwung watershed’s vulnerability to flash floods.

Mass movement hazard & global climate change: Physical-deterministic modelling study of the rest and be thankful pass, Scotland UK

Mass movements frequently affect infrastructure corridors, particularly so in mountainous areas. The A83 at the Rest and be Thankful pass (Scotland, UK) is a prime example of a road experiencing frequent closure due to precipitation-triggered landslides and debris flows. The closures result in long (>90 km) diversions and severe economic losses (>£60 million). To ensure persistence of transport services, future engineered interventions and maintenance schedules must anticipate the potential consequences of climate change that affects the frequency and magnitude threshold precipitation events that are capable of triggering mass movement. In turn this requires a better understanding of the hydrological implications of climate change on precipitation-triggered landslides and debris flows. The area is prone to mass movements due to steep topography, the glacial history, reworked deposits of mud and siltstone, and landslide-triggering precipitation. The objective here is to assess the severity of future events, i.e. the coming decades, was assessed by means of an event-based runout model for selected events that were identified using a present-day precipitation threshold applied to hydro-meteorological forecasts for this part of the UK. A convex hull method was applied to these forecasts to create a comprehensive selection of modelled events and a much reduced computational load. This selection is considered to be representative for the largest variation within the forecasts.Results show that 61 mm of daily precipitation will likely trigger mass movement. This threshold reduces by 0.15 mm for every millimetre accounted for in an antecedent precipitation index calculated based on 8 antecedent days. On the basis of the hydro-meteorological forecasts it can be concluded that the frequency of these trigger conditions gradually declines, as seven out of the eleven RCM (Regional Climate Model) configurations show longer recurrence intervals. This corresponds with the decline in the modelled future precipitation. However, it is observed that, for scenarios with lower average daily precipitation and antecedent precipitation indices, event severity is likely to increase as intense daily precipitation follows drier antecedent conditions. For scenarios with higher average daily precipitation and antecedent precipitation indices, persistent wetting appears to expand source areas and increases material entrainment. The simulations show that the main source areas, situated between 300 and 500 metre elevation, extend upslope when the antecedent precipitation regime has peak precipitation values in the latter stages of the 8 antecedent days.

Estimating multi-scale irrigation amounts using multi-resolution soil moisture data: A data-driven approach using PrISM

Irrigated agriculture is the primary driver of freshwater use and is continuously expanding. Precise knowledge of irrigation amounts is critical for optimizing water management, especially in semi-arid regions where water is a limited resource. This study proposed to adapt the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM was originally conceived to correct precipitation products, assimilating Soil Moisture (SM) observations into an antecedent precipitation index (API) formula, using a particle filter scheme. This novel application of PrISM uses initial precipitation and SM observations to detect instances of water excess in the soil (not caused by precipitation) and estimates the amount of irrigation, along with its uncertainty. This newly proposed approach does not require extensive calibration and is adaptable to different spatial and temporal scales. The objective of this study was to analyze the performance of PrISM for irrigation amount estimation and compare it with current state-of-the-art approaches. To develop and test this methodology, a synthetic study was conducted using SM observations with various noise levels to simulate uncertainties and different spatial and temporal resolutions. The results indicated that a high temporal resolution (less than 3 days) is crucial to avoid underestimating irrigation amounts due to missing events. However, including a constraint on the frequency of irrigation events, deduced from the system of irrigation used at the field level, could overcome the limitation of low temporal resolution and significantly reduce underestimation of irrigation amounts. Subsequently, the developed methodology was applied to actual satellite SM products at different spatial scales (1 km and 100 m) over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer in Catalunya, Spain, where in situ irrigation amounts were available for various years. The validation resulted in a total Pearson’s correlation coefficient (r) of 0.80 and a total root mean square error (rmse) of 7.19 mm∕week for the years from 2017 to 2021. Additional validation was conducted at the field level in the Segarra-Garrigues irrigation district using in situ data from a field where SM profiles and irrigation amounts were continuously monitored. This validation yielded a total bi-weekly r of 0.81 and a total rmse of −9.34 mm∕14-days for the years from 2017 to 2021. Overall, the results suggested that PrISM can effectively estimate irrigation from SM remote sensing data, and the methodology has the potential to be applied on a large scale without requiring extensive calibration or site-specific knowledge.

Satellite remote sensing model for estimating canopy transpiration in cypress plantation using in situ sap flow observations and forest inventory

Transpiration (Et) in forests accounts for a significant fraction of evapotranspiration. However, because Et is influenced by meteorological factors and the physiological response of vegetation, developing an accurate model for predicting Et presents a challenge. In this study, we developed a model to estimate Et specific to cypress plantation forests using satellite remote sensing and sap flow data in Japanese cypress plantations composed of a single forest species. The contribution of Et to meteorological (Etbase) and plant physiology (Etleaf) was determined using the decoupling index along with the surface temperature. Using the Forest Inventory (FI), in which the extent of growth of each species, including cypress, was surveyed and Land Remote Sensing Satellite imagery of the FI range was obtained, an index of activity estimation for cypress trees was developed. The sapwood area, which significantly influences Et, was calculated from the tree age obtained from FI or diameter at breast height and incorporated into the model. The developed model exhibited a high correlation of r = 0.74–0.88 with the measured Et. The antecedent precipitation index (API), which represents the wetness of the land surface, can be used to evaluate the confidence level and correction coefficient (if needed) of model estimates. When corrected by API, Et could be estimated with even higher accuracy (r = 0.76–0.89). The model developed in this study considers the physiological responses of vegetation and plant species, and the same approach can be used to develop models to estimate Et for other tree species. Additionally, our model uses reflectance at wavelengths close to visible light and surface temperature and can, therefore, be readily applied to other remote sensing methods, such as unmanned aerial vehicles and airborne methods. The model developed in this study can be used with FI data to estimate Et by tree species to provide detailed and accurate estimates of Et.

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems.

Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergize with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition, as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process-based models of chemical fate that do not typically allow for such high-throughput applications. Here, we describe a Bayesian network model that does. It incorporates a two-step univariate regression model based on a 30-day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in-stream exposures. When relating to aquatic risks, the influence of these processes is reduced and distributions of risk are dominated by effect-related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient >1) was 8%-12%. This percentage further increased to 30%-35% when a more conservative precautionary factor of 100 instead of 30 was used. Integr Environ Assess Manag 2024;20:384-400. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Three-dimensional hydrological thresholds to predict shallow landslides

Past studies have focused on the importance of hydrological variables in analyzing landslide initiation condition. Even though precipitation is the main driver of shallow landslides and debris flows, use of only rainfall-based parameters has shown some limitations. Soil moisture has been used widely to improve threshold detection capabilities. Since soil moisture directly reflects the wetness status of the ground, it can be used to identify pore pressure fluctuations more effectively. This study used rainfall and soil moisture simultaneously to capture landslide initiation conditions in detail. Results showed that continued rainfall on the day of landslide leaded to a sudden increase in soil moisture, and that soil moisture increments (∆SM) were positive in 155 out of 170 landslide cases (91%). Two simple thresholds (daily precipitation over 40 mm, ∆SM over 0) and daily precipitation (P), Antecedent Precipitation Index (API), ∆SM-based three-dimensional threshold planes having 5%, 20% probability levels were applied and compared. With respect to false alarms (FA), P-based threshold was most effective among the single thresholds (FA ranging from 24 to 28 from September 2016 to December 2019 at five validation locations). Combining P- and ∆SM-based thresholds, FA reduced without compromising the detection accuracy (2 to 3 reduction in FA). Additionally combining three-dimensional threshold with 20% probability level, FA reduced significantly (ranging from 12 to 16), at the cost of two detection failures. These findings demonstrate the need for combining precipitation and soil moisture to determine landslide thresholds.

On the relation between antecedent basin conditions and runoff coefficient for European floods

The event runoff coefficient (i.e. the ratio between event runoff and precipitation that originated the runoff) is a key factor for understanding basin response to precipitation events. Runoff coefficient depends on precipitation intensity and duration but also on specific basin geohydrology attributes (including soil type, geology, land cover, topography) and last but not least, antecedent (or pre-storm) conditions (i.e., the amount of water stored in the different hydrological compartments, like the river, groundwater, soil and snowpack). The relation between runoff coefficient and basin pre-storm conditions is critical for flood forecasting, yet, the understanding of where, when and how much basin pre-storm conditions control runoff coefficients is still an open question.Here, we tested the control of basin pre-storm conditions on runoff coefficient for 60620 flood events across 284 basins in Europe. To do so, we derived basin pre-storm conditions from different proxies, namely: antecedent precipitation; surface and root zone soil moisture from hydrological models, reanalyses and land surface models also ingesting satellite observations; pre-storm river discharge, and pre-storm total water storage anomalies. We evaluated the coupling strength between runoff coefficient and pre-storm conditions proxies in relation to five classes of European basins, defined based on land use and soil type (as indexed by the Soil Conservation Service curve number CN), topography, hydrology and long-term climate and tested their ability to explain stormflow volume variability.We found that precipitation explains relatively well the stormflow volumes for both small and large events but not very well the peak discharge, especially for large floods. The runoff coefficient of events shows different distributions for the five different classes and correlates well with deep soil storages (such as root-zone soil moisture and pre-storm total water storage anomalies), pre-storm river discharge, and pre-storm snow water equivalent. Overall, these correlations depend on the class. Poor correlations are found against antecedent precipitation index despite its wide use in the hydrological community. Seasonal and interannual climate variability exert a key role on the coupling strength between runoff coefficient and pre-storm conditions by inducing sharp changes in the correlation with season and climate.These results increase our understanding of the coupling between pre-storm conditions and runoff coefficients. This will aid flood forecasting, hydrological and land surface model calibration, and data assimilation. Furthermore, these findings can help us to better interpret future flood projections in Europe based on expected changes in long and short-term climatic drivers.

Probability of rainstorm and flood disasters due to extreme precipitation in Fen River Basin, China.

Analysis of the probability of extreme precipitation events leading to rainstorm and flood disasters can aid in disaster prevention policy development. Using daily precipitation data from 16 meteorological stations from 1960 to 2019, we calculated eight extreme precipitation indices to analyze the spatio-temporal characteristics of extreme precipitation in the Fen River Basin (FRB) through ensemble empirical mode decomposition and Kriging interpolation. Extreme precipitation events and disasters were defined based on a combination of the antecedent precipitation index (API) and extreme precipitation on the event day and classified; extreme precipitation and the API were ranked from small to large and classified into dry, wet, and moderate (mod) precipitation periods, respectively, yielding nine extreme precipitation event categories. The probability of disasters caused by different types of extreme precipitation events was calculated using a binomial distribution. The results are as follows: (1) between 1960 and 2019, except for extreme precipitation period length, which continuously increased, the extreme precipitation indices changed from a downward to an upward trend since the 1980s. All extreme precipitation indices showed similar interannual variation over short periods and different interdecadal variation over long periods. (2) The extreme precipitation indices showed latitudinal and zonal spatial divergence patterns, but different spatial characteristics were observed around the 1980s. (3) More than 70% of extreme precipitation events in the midstream and downstream fell into four categories: "dry-dry," "dry-mod," "mod-dry," and "mod-mod." (4) A single category VII (VIII) extreme precipitation event in the midstream (downstream) had a maximum probability of causing disaster of 14%. When more than four extreme precipitation events occurred in a year, the probability of one disaster was the highest and that of four or more disasters was < 0.1%. The probability of rainstorm and flood disasters increased gradually with increasing frequency of annual extreme precipitation events.

Drivers of future streamflow changes in watersheds across the Northeastern United States

AbstractAccurate projections of streamflow, which have implications for flooding, water resources, hydropower, and ecosystems, are critical to climate change adaptation and require an understanding of streamflow sensitivity to climate drivers. The northeastern United States has experienced a dramatic increase in extreme precipitation over the past 25 years; however, the effects of these changes, as well as changes in other drivers of streamflow, remain unclear. Here, we use a random forest model forced with a regional climate model to examine historical and future streamflow dynamics of four watersheds across the Northeast. We find that streamflow in the cold season (November–May) is primarily driven by 3‐day rainfall and antecedent wetness (Antecedent Precipitation Index) in three rainfall‐dominant watersheds, and 30‐day rainfall, antecedent wetness, and 30‐day snowmelt in the fourth, more snowmelt‐dominated watershed. In the warm season (June–October), streamflow is driven by antecedent wetness and rainfall in all watersheds. By the end of the century (2070–2099), cold season streamflow depends on the importance placed on snow in the machine learning model, with changes ranging from −7% (with snow) to +40% (without snow) in a single watershed. Simulated future warm season streamflow increases in two watersheds (56% and 193%) due to increased precipitation and antecedent soil wetness, but decreases in the other two watersheds (−6% and −27%) due to reduced precipitation.

An integrated process-based framework for flood phase segmentation and assessment

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (IF) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R2 = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation.

Seasonal prediction of crop yields in Ethiopia using an analog approach

As Ethiopia's population grows, crop yield predictions are becoming increasingly important for national food security. Accurate, easy-to-implement, and computationally efficient forecast approaches are desirable for broad applications in emerging regimes like Ethiopia. In this study, we develop and test an analog approach for pre-season crop yield prediction conditioned on antecedent precipitation and planting time soil moisture content indices to guide cultivation decision making. Historical planting time soil moisture at four selected sites were simulated using the Coupled Routing and Excess STorage (CREST) hydrological model and classified into five levels. Likewise, a historical crop yield database for each of the five classes of planting time soil moisture were constructed using the Decision Support System for Agrotechnology Transfer (DSSAT) agricultural model. Adopting maize as a representative crop, analog models based on different indexes or predictors with various lead times were constructed and used to conduct hindcasts during 1979–2014 and real-time forecast in 2018 and 2019. Both the hindcast and real-time forecasts were then evaluated against yield observations. To verify the applicability at locations with various environments, the analog models were then applied in different Agroecological Zones. The analog models were shown to be accurate and easy to implement, which may incentivize adoption by local extension agents and regional agricultural agencies to inform farmers’ crop choices.

Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index

ABSTRACT This study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local characteristics of soil moisture development. An advanced antecedent precipitation index (API) is utilized to generate a global soil moisture product of high temporal resolution with the Global Precipitation Measurement (GPM) Missions Integrated Multi-Satellite Retrievals for GPM (IMERG) as main driver. The resulting global GPM API data set is compared against in situ measurements from the International Soil Moisture Network (ISMN) and is also evaluated against the soil moisture data set from the European Space Agency’s Climate Change Initiative (ESA CCI SM). The study shows that with empirically derived dampening factors the GPM API achieves a mean ubRMSD across the utilized in situ stations in different climates and vegetation zones of 4.68 and a bias of 0.88 . The data set clearly represents the local soil moisture schemes with seasonal variations. When comparing with ESA CCI SM, the GPM API does perform better at the measurement sites concerning bias, correlation and error values. The data set is in most parts negatively biased compared to the ESA CCI SM, however better matches the mean soil moisture at ISMN stations. Overall, the GPM API delivers a very promising global, hourly surface soil moisture product at 0.1 0.1 spatial resolution.

Residual-Oriented Optimization of Antecedent Precipitation Index and Its Impact on Flood Prediction Uncertainty

Antecedent moisture conditions are essential in explaining differences in the translation of flood-producing precipitation to floods. This study proposes an empirical residual-oriented antecedent precipitation index (RAPI) to estimate and further link antecedent moisture conditions with flood predictive uncertainty. By developing a fully kernel-based residual error model without functional presumptions, the proposed RAPI is calibrated as the regressor of the deterministic model residual. Furthermore, the MI-LXPM algorithm is applied to search for optimal parameters in mixed-integer constraints. The rationality of the proposed framework is demonstrated by its application to a case study in South-East China. The quality of probabilistic streamflow predictions is then quantified using reliability, precision, and the NSE of the prediction mean. The results show that the RAPI closely connects to the uncertainty of hourly flood prediction as a proxy of antecedent soil moisture. The influence of RAPI is mainly on the precision and unbiasedness of flood prediction. Compared with the deterministic model output, the RAPI provides a better flood prediction of small to median flood events as a regressor. Along with the proposed date-driven residual error model, the framework can be applied to any pre-calibrated hydrological model and help modelers achieve high-quality probability flood prediction.

Nonlinear relationships of runoff and sediment yield with natural and anthropogenic factors at event scale

Understanding how natural and anthropogenic factors affects the watershed runoff and sediment yield at event scale can provide useful insights into watershed hydrological processes to guide watershed management. This paper investigates the nonlinear relationships between natural and anthropogenic factors and event runoff and sediment yield with a boosted regression tree (BRT) model for 38 watersheds within the Loess Plateau in China during 2006 to 2016. The BRT model captures the relative importance of each natural and anthropogenic factors to the variability in runoff and sediment yield. The results show that these relationships are complex and highly nonlinear. The event runoff was most related to NDVI in the grass land (NDVI_g), with a 21.3% contribution, followed by 3-day antecedent precipitation index (AP3), 10-day antecedent precipitation index (AP10), precipitation (P), and 7-day antecedent precipitation index (AP7). For event sediment yield, the strongest factor is AP3, with contribution of 33.3%, followed by P, NDVI in the forest land (NDVI_f), AP7, and NDVI in the crop land (NDVI_c). The marginal effect curves produced by the BRT are often characterized by thresholds. For instance, NDVI_f has the greatest effect on event sediment yield reduction when NDVI_f = 0.42, suggesting that a very intense green coverage is not necessary to achieve maximal soil erosion control. Vegetation coverage and meteorological factors can explain 54.7% of event runoff variation and 55.6% of event sediment yield variation. Our study identify the nonlinear relationships between runoff and sediment yield with vegetation cover and meteorological factors and provide scientific support for the planning of subsequent reforestation projects in the Loess Plateau. This article is protected by copyright. All rights reserved.

Controlling Factors and Characteristics of Peak Runoff in an Alpine Headwater Under the Asian Monsoon Climate

Understanding the mountain hydrological cycle, including runoff processes, is important for water-related disaster prevention. Although the process of peak runoff generation is closely related to water hazards, this process has not been clarified in alpine headwaters with large amounts of precipitation. In this study, we conducted hydrological observations to clarify runoff characteristics and factors that determine peak runoff in an alpine headwater under the Asian monsoon climate. Total precipitation during the summer period (3 months) was 1581.4 mm, and the water runoff responded quickly and clearly to rainfall events. Focusing on baseflow, the runoff was terminated when the snow cover area decreased. This suggested that snowmelt water plays an important role in maintaining baseflow in alpine headwaters under the Asian monsoon climate, like other alpine areas worldwide. In addition, peak runoff was not significantly correlated with soil wetness (as indicated by the antecedent precipitation index), whereas it was correlated with the amount of rainfall just before the generation of peak runoff. Therefore, the amount of rainfall before peak runoff in a single event was important in determining peak runoff. Focusing on the snowmelt season, we confirmed that the runoff increased even during small rainfall events. This indicated that snowpack melting is another factor determining peak runoff when the snowpack remains in the catchment. Considering the immediate runoff generation after rainfall events, direct observation of hydrometeorological data in situ is crucial to predict water-related disasters and consider countermeasures in alpine regions.