Antecedent Precipitation Index Research Articles

Research on flood forecasting based on flood hydrograph generalization and random forest in Qiushui River basin, China

Abstract At present, the use of hydrological models is the main technical approach for real-time flood forecasting. However, in semi-arid and arid areas, the use of the hydrological model is restricted by technical and data conditions. With the accumulation of hydrological data deluge, making full use of historical data and mining potential hydrological laws, causal relationships and other valuable information behind them provide new ideas for real-time flood forecasting in the study area. This paper develops a hybrid flood forecasting model that combines the flood hydrograph generalization method and random forest in the Qiushui River basin in the middle reaches of the Yellow River. The performance of this hybrid model is compared to that of the antecedent precipitation index model. For the development of these models, 23 flood events occurring from 1980 to 2010 are selected, of which 18 are used for calibration and 5 are used for validation. The results show that the hybrid model yields accurate predictions. And the comparison shows that the hybrid model performs better than the empirical model in the Qiushui River basin. Thus, this study provides a method for improving the accuracy of flood forecasting.

Modelling Actual Evapotranspiration Seasonal Variability by Meteorological Data-Based Models

This study aims at illustrating a methodology for predicting monthly scale actual evapotranspiration losses only based on meteorological data, which mimics the evapotranspiration intra-annual dynamic. For this purpose, micrometeorological data at the Rollesbroich and Bondone mountain sites, which are energy-limited systems, and the Sister site, a water-limited system, have been analyzed. Based on an observed intra-annual transition between dry and wet states governed by a threshold value of net radiation at each site, an approach that couples meteorological data-based potential evapotranspiration and actual evapotranspiration relationships has been proposed and validated against eddy covariance measurements, and further compared to two well-known actual evapotranspiration prediction models, namely the advection-aridity and the antecedent precipitation index models. The threshold approach improves the intra-annual actual evapotranspiration variability prediction, particularly during the wet state periods, and especially concerning the Sister site, where errors are almost four times smaller compared to the basic models. To further improve the prediction within the dry state periods, a calibration of the Priestley-Taylor advection coefficient was necessary. This led to an error reduction of about 80% in the case of the Sister site, of about 30% in the case of Rollesbroich, and close to 60% in the case of Bondone Mountain. For cases with a lack of measured data of net radiation and soil heat fluxes, which are essential for the implementation of the models, an application derived from empirical relationships is discussed. In addition, the study assessed whether this variation from meteorological data worsened the prediction performances of the models.

Effectof Unit Hydrographs and Rainfall Hyetographs on Critical Rainfall Estimates of Flash Flood

To obtain critical rainfall (CR) estimates similar to the rainfall value that causes minor basin outlet flooding, and to reduce the flash flood warning missed/false alarm rate, the effect of unit hydrographs (UHs) and rainfall hyetographs on computed threshold rainfall (TR) values was investigated. The Tanjia River basin which is a headwater subbasin of the Greater Huai River basin in China was selected as study basin. Xin’anjiang Model, with subbasins as computation units, was constructed, and time-variant distributed unit hydrographs (TVUHs) were used to route the channel network concentration. Calibrated Xin’anjiang Model was employed to derive the TVUHs and to obtain the maximum critical rainfall duration (Dmax) of the study basin. Initial soil moisture condition was represented by the antecedent precipitation index (Pa). Rainfall hyetographs characterized by linearly increasing, linearly decreasing, and uniform hyetographs were used. Different combinations of the three hyetographs and UHs including TVUHs and time-invariant unit hydrographs (TIVUHs) were utilized as input to the calibrated Xin’anjiang Model to compute the relationships between TR and Pa (TR-Pa curves) by using trial and error methodology. The computed TR-Pa curves reveal that, for given Pa and UH, the TR corresponding to linearly increasing hyetograph is the minimum one. So, the linearly increasing hyetograph is the optimum hyetograph type for estimating CR. In the linearly increasing hyetograph context, a comparison was performed between TR-Pa curves computed from different UHs. The results show that TR values for different TIVUHs are significantly different and the TR-Pa curve gradient of TVUHs is lower than that of TIVUHs. It is observed that CR corresponds to the combination of linearly increasing hyetograph and TVUHs. The relationship between CR and Pa (CR-Pa curves) and that between CR and duration (D) (CR-D curves) were computed. Warnings for 12 historical flood events were performed. Warning results show that the success rate was 91.67% and that the critical success index (CSI) was 0.91. It is concluded that the combination of linearly increasing hyetograph and TVUHs can provide the CR estimate similar to the minimum rainfall value necessary to cause flash flooding.

Monitoring soil moisture at the catchment scale – A novel approach combining antecedent precipitation index and radar-derived rainfall data

Knowledge about soil moisture is important for event-based rainfall-runoff models but monitoring conditions at the catchment scale is not a trivial task. Soil moisture is highly variable in space and time, particularly in dry climates with seasonal and spatially heterogeneous rainfall. Point measurements are difficult to upscale, and remotely sensed (RS) data often lack in spatial or temporal resolution for local or regional studies. Longer latency periods – the time required before data becomes available – of some RS data make them less applicable to time-sensitive analyses such as flash flood forecasting. This study evaluated a novel approach for estimating catchment-scale volumetric soil moisture using an antecedent precipitation index (API) -based model. The model was trained and tested using in-situ soil moisture measurements collected during a 3-month field sampling campaign in a 142 km2 study area in central New Mexico. The calibrated model was applied at the catchment scale to produce soil moisture grids from radar-derived rainfall estimates. Model performance, resolution and latency were compared to satellite-based soil moisture estimates. Benefits of the proposed new method include high spatial resolution (1 × 1 km or less depending on the precipitation data source) and high prediction accuracy (root mean square errors 0.014–0.018 m3/m3). Given the short latency period for radar-derived rainfall data, the method has potential for use in operational flood risk assessment and forecasting.

Can We Use Satellite-Based Soil-Moisture Products at High Resolution to Investigate Land-Use Differences and Land–Atmosphere Interactions? A Case Study in the Savanna

The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.

Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

AbstractInitial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

Applicability of Hydrological Models for Flash Flood Simulation in Small Catchments of Hilly Area in China

Abstract Flash flood in small catchments of hilly area is an extremely complicated nonlinear process affected by catchment properties and rainfall spatio-temporal variation characteristics including many physical-geographical factors, and thus accurate simulation of flash flood is very difficult. Given the fact that hundreds of hydrological models are available in the literature, how to choose a suitable hydrological model remains an unsolved task. In this paper, we selected five widely used hydrological models including three lumped hydrologic models, a semi-distributed hydrological model and a distributed hydrological model for flash flood simulation, and studied their applicability in fourteen typical catchments in hilly areas across China. The results show that the HEC-HMS distributed hydrological model outperforms the other models and is suitable to simulate the flash floods caused by highly intense rainfall. The Dahuofang model (lumped) has higher precision in peak runoff time simulation. However, its performance is quite poor on the flood volume simulation in the small catchments characterized by intense vegetation coverage and highly developed stream network. The Antecedent precipitation index and Xinanjiang models (lumped) can obtain good simulation results in small humid catchments as long as long-term historical precipitation and runoff data are provided. The TOPMODEL also shows good performance in small humid catchments, but it is unable to simulate the flash floods characterized by the rapid rise and recession. Our results could be very beneficial in practice, since these provide a solid foundation in the selection of hydrological model for flash flood simulation in small catchments in hilly area.

Calculation of Early Warning Indicators for Small Watersheds Based on Mathematical Statistics

The early warning and forecasting of mountain flood disasters is an indispensable part of mountain flood prevention and control work. Reasonable selection of rainfall warning indicators is the key to mountain flood warning and forecasting work. This paper proposes a method for calculating the critical rainfall warning indicator based on mathematical statistics. That is, the characteristic period is determined based on the confluence time and watershed characteristics of the upstream watershed of the disaster prevention object. The stimulated rainfall of each characteristic period and its corresponding antecedent precipitation index is calculated based on the rainfall data of the flooding. Using the regression analysis method to draw the critical rainfall line, calculate the critical rainfall warning indicator and take the typical disaster prevention objects of Chahekou Village in Muping District of Yantai City, Shandong Province as an example, and the historical rainfall records of 2014-2018 were used as verification data to verify the early warning indicators. The results show that the critical rainfall accuracy calculated by this method is about 80%, which can provide reference for the application of early warning indicators for mountain flood disasters.

Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products

AbstractIn this study, the impact of various rescaling approaches in the framework of data fusion is explored. Four different soil moisture products (Advanced Scatterometer; Advanced Microwave Scanning Radiometer for EOS, AMSR‐E; Antecedent Precipitation Index; and Global Land Data Assimilation System‐NOAH) are fused. The systematic differences between products are removed before the fusion utilizing various rescaling approaches focusing on different methods (regression, variance/cumulative distribution function (CDF) matching, multivariate adaptive regression splines, and support vector machines based), stationarity assumptions (constant or time‐varying rescaling coefficients), and time‐frequency techniques (periodic or nonperiodic high‐ and low‐frequency components). Given that statistical descriptions (e.g., standard deviation and correlation coefficient) of reference data sets are utilized in rescaling approaches, the precision of the selected reference data set also impacts the final fused product precision. Experiments are validated over 542 soil moisture monitoring sites selected from the International Soil Moisture Network data sets between 2007 and 2011. Overall, results highlight the importance of reference data set selection—particularly that a more precise reference product yields a higher precision fused soil moisture product. This conclusion is sensitive neither to the number of fused products nor the rescaling procedure. Among rescaling approaches, the precision of fused products is most affected by the choice of rescaling stationary assumption and time‐frequency decomposition technique. Variations in rescaling methods have only a small impact on the precision of pair fused products. In contrast, utilizing a time‐varying stationary assumption and nonperiodic decomposition technique produces correlation improvements of 0.07 [−] and 0.02 [−], respectively, versus the other widely implemented rescaling approaches.

Estimation of soil moisture using modified antecedent precipitation index with application in landslide predictions

Soil moisture plays a key role in land-atmosphere interaction systems. Although it can be estimated through in situ measurements, satellite remote sensing, and hydrological modelling, using indicators to index soil moisture conditions is another useful way. In this study, one of these indicators, the antecedent precipitation index (API), is explored. Modifications were proposed to the conventional version of API by introducing two parameters to make it more in line with the physical process. First, the recession coefficient is allowed to vary with the change of air temperature, which could take into account the variation of the evapotranspiration process. Second, the API value is restricted by the maximum value of API, accounting for the maximum water holding capacity of the soil. The modified API was then calibrated and validated by comparing with the in situ measured soil moisture. The better correlation between these two datasets demonstrates that the modified API could better indicate soil moisture conditions, compared with the conventional API. The capability of the modified API to index soil moisture conditions was further explored by applying it to landslide predictions in the Emilia-Romagna region, northern Italy. Here, the recent 3-day rainfall vs the antecedent soil wetness thresholds (RS thresholds) were constructed, in which the soil wetness is indexed by the modified API. The validation of RS thresholds was carried out with the use of the contingency matrix and receiver operating characteristic (ROC) curves. By comparing the prediction performance between RS thresholds and rainfall thresholds, it is found that RS threshold could provide better prediction capabilities in terms of higher hit rate and lower false alarm rate. The positive results indicate that the modified API could provide superior performance of indexing soil moisture conditions, demonstrating the effectiveness of the proposed modifications.

Evaluation of Remotely-Sensed and Model-Based Soil Moisture Products According to Different Soil Type, Vegetation Cover and Climate Regime Using Station-Based Observations over Turkey

This study evaluates the performance of widely-used remotely sensed- and model-based soil moisture products, including: The Advanced Scatterometer (ASCAT), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), the European Space Agency Climate Change Initiative (ESA-CCI), the Antecedent Precipitation Index (API), and the Global Land Data Assimilation System (GLDAS-NOAH). Evaluations are performed between 2008 and 2011 against the calibrated station-based soil moisture observations collected by the General Directorate of Meteorology of Turkey. The calibration of soil moisture observing sensors with respect to the soil type, correction of the soil moisture for the soil temperature, and the quality control of the collected measurements are performed prior to the evaluation of the products. Evaluation of remotely sensed- and model-based soil moisture products is performed considering different characteristics of the time series (i.e., seasonality and anomaly components) and the study region (i.e., soil type, vegetation cover, soil wetness and climate regime). The systematic bias between soil moisture products and in situ measurements is eliminated by using a linear rescaling method. Correlations between the soil moisture products and the in situ observations vary between 0.57 and 0.87, while the root mean square errors of the products versus the in situ observations vary between 0.028 and 0.043 m3 m−3. Overall, according to the correlation and root mean square error values obtained in all evaluation categories, NOAH and ESA-CCI soil moisture products perform better than all the other model- and remotely sensed-based soil moisture products. These results are valid for the entire study time period and all of the sub-categories under soil type, vegetation cover, soil wetness and climate regime.

Sources of soil dissolved organic carbon in a mixed agricultural and forested watershed in Rwanda

Dissolved organic carbon (DOC) plays a key role in linking terrestrial and aquatic carbon cycles. Most of the work on soil and water DOC has been conducted in temperate watersheds. There is still a gap in knowledge on DOC dynamics within the tropics. This study assesses water extractable organic carbon (WEOC) in topsoils and describes the relationship between WEOC and land use/land cover (LULC), slope position, curvature and soil properties using linear regression in the Rukarara River Watershed (RRW) in Rwanda. The study analyzes DOC concentration in soil percolation water (pDOC) and describes its relationship with antecedent precipitation index (API) and mean antecedent temperature (MAT) within the watershed using quadratic regression. Generalized linear model (GLM) and linear mixed effect model (LME) with site and/or LULC random effects are used to predict WEOC within the watershed. WEOC concentrations range from 124 to 855 mgC/L in the study area. The highest WEOC concentrations were observed in natural forest, followed by tree plantations, tea plantations and croplands. t-test results did not reveal a significant difference between concentrations of WEOC in valleys, upper slopes, ridges, flat, concave and convex areas in the Rukarara River Watershed (RRW). Considering the relationship between WEOC and soil properties, significant positive correlation coefficients were 0.60, 0.53, 0.50, and 0.36 respectively for the total organic carbon (TOC), the total nitrogen (TN), the cation exchange capacity (CEC), and the aluminum (Al). The best predictor WEOC as a function of soil properties was the generalized linear model (GLM) and indicated soil TOC as the overarching soil factor of WEOC in the RRW by 71%. The pDOC concentration ranges between 0.34 and 10.03 mgC/L and its relationship with both API and MAT was concave upward. APIs explained 12 to 17% of the pDOC variation in the RRW whereas MATs explained 8 to 25%, the natural forest site showing the highest values and the cropland site the lowest values. This result means that a conversion from forest to cropland within the RRW could decrease DOC in both soils and in percolation water. Also, an increase of precipitation and temperature up to respective optima in the RRW, could increase DOC in percolation water and consequently in streams. This increase of instream DOC can impact the water quality of the Rukarara River and its streams, with implications for their ecological function. Strategies of land management and water resources should be enhanced to preserve soil and water quality in the RRW.

Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging

AbstractSoil moisture spatial patterns with length scales of 1‐100 km influence hydrological, ecological, and agricultural processes, but the footprint or support volume of existing monitoring systems, for example, satellite‐based radiometers and sparse in situ monitoring networks, is often either too large or too small to effectively observe these mesoscale patterns. This measurement scale gap hinders our understanding of soil water processes and complicates calibration and validation of hydrologic models and soil moisture satellites. One possible solution is to utilize geostatistical techniques that have proven effective for mapping static patterns in soil properties. The objective of this study was to determine how effectively dynamic, mesoscale soil moisture patterns can be mapped by applying regression kriging to the data from a sparse, large‐scale in situ network. The fully automated system developed here uses several data sets: daily soil moisture measurements from the Oklahoma Mesonet, sand content estimates from the Natural Resource Conservation Service Soil Survey Geographic Database, and an antecedent precipitation index computed from National Weather Service multisensor precipitation estimates. A multiple linear regression model is fitted daily to the observed data, and the residuals of that model are used in a semivariogram estimation and kriging routine to produce daily statewide maps of soil moisture at 5‐, 25‐, and 60‐cm depths at 800‐m resolution. During over 3 years of operation, this mapping system has revealed complex, dynamic, and depth‐specific mesoscale patterns, reflecting the shifting influences of both soil texture and precipitation, with a mean absolute error of ≤0.0576 cm3/cm3 across all three depths.

A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation

The main sources of global soil moisture information are remote sensing observations and land surface model estimates. Data assimilation (DA) aims at optimally combining these data sources through statistical merging. To properly parameterize such merging, one needs to obtain accurate knowledge of model and observation uncertainties, which is the crux of a successful DA system. In this paper, we propose a new Monte Carlo based adaptive Kalman filtering framework (MadKF) that estimates model and observation uncertainties (Q and R) and updates soil moisture forecasts simultaneously. Spatially distributed uncertainties are estimated by applying triple collocation analysis (TCA) to Monte Carlo simulations of the model open-loop, the model analysis, and the observation time series at each grid cell. Error cross-covariances, which are inevitable between these time series, are diagnosed from their ensembles and used to iteratively correct biases they cause in Q and R estimates, and hence, in the Kalman filter gain. The proposed MadKF is tested in a synthetic environment and by assimilating real satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) into the Antecedent Precipitation Index (API) model forced with daily aggregated satellite precipitation. Synthetic experiments indicate a good convergence of Q and R estimates. Internal DA diagnostics, i.e., the innovation auto-correlation (IAC) and the variance of the normalized innovations, asymptotically converge to their desired values, which indicates that the filter is operating near its optimum and reliably estimates analysis uncertainty. Real-data experiments assimilating ASCAT observations into the API model further indicate that the MadKF is robust against observation error auto-correlations, which typically cause problems in conventional IAC-tuning based adaptive filtering approaches. A performance evaluation over 264 in situ sites within the contiguous United States shows that the MadKF leads to a significant skill gain in surface soil moisture estimation. The MadKF is also potentially applicable when assimilating Level 1 backscatter or brightness temperature observations, or when updating/assimilating other geophysical variables.

Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

Drought and cool temperature cue general flowering synergistically in the aseasonal tropical forests of Southeast Asia

AbstractThe family Dipterocarpaceae, along with species from many other families, flower synchronously at irregular intervals of several years in the extensive humid forests of Southeast Asia. The intermittent mast seeding in these forests satiates seed predators whose populations remain low due to starvation in non masting periods, effectively increasing seed survival and seedling recruitment. A previous study demonstrated that a synergistic interaction between drought and cool temperatures, instead of either climate factor alone, best explains the occurrence of general flowering events observed over 14 years at the Pasoh Research Forest, Malaysia. However, several measures of drought were proposed and which drought measure should be used to explain general flowering remains untested. Here we examined the relationship between flowering of five Shorea species and three drought indices, the difference between the mean daily accumulation of precipitation and a threshold level of precipitation, water deficit and antecedent precipitation index. Our analyses confirmed that for most of Shorea species we studied the synergistic model that considers both cool temperature and drought performs the best regardless of the choice of drought index. In addition, a simplest drought measure using cumulative precipitation is sufficient to explain flowering activities in Shorea species.

Rainfall analysis of debris flows in the Obří důl Valley in the Krkonoše Mts., Czechia

Rainfall is the most important triggering factor responsible for the occurrence of debris flows in the Obři důl Valley in the Krkonose Mts. The critical rainfall conditions for slope failures are not the same for different debris flows, and may be strongly influenced by regional geological and geomorphological conditions. Nevertheless, analysis of hourly intensities, daily rainfall, cumulative data and the antecedent precipitation index (API) revealed that several of the above-mentioned factors are necessary to trigger the debris flow. On the other hand, a significant amount of daily rainfall (e.g. 225 mm) could trigger a debris flow without the support of any other rainfall characteristics in the monitored area and period under review. We used several rain gauges from the study area but the local differences in rainfall were so high that data from more remote stations was difficult to include in the Obři důl Valley. This is why only a limited amount of precise data is available for some years.

Comparison of modeling approaches for flood forecasting in the High Atlas Mountains of Morocco

In the Mediterranean region, floods are causing extended damages to the population and infrastructures. In Morocco, only a few studies have been conducted to understand flood processes while the vulnerability to floods is high. The goals of this study are to compare two modeling approaches for floods using either lumped or spatial rainfall and also to evaluate hydrological forecast capabilities. The Rheraya research catchment is characterized with steep slopes, altitudes ranging from 1027 to 4167 m, and a strong variability of rainfall. The lumped and distributed models provided similar results and reproduced well a sample of six flood events recorded in 2014. However, the distributed model provided the best estimation of the initial conditions, estimated from the ESA-CCI satellite soil moisture product and the Antecedent Precipitation Index. The validation of the lumped and the distributed models, using ESA-CCI to initialize the models, provides a Nash coefficient of 0.61 and 0.63 respectively. Then, two meteorological forecasts provided by the AROME and ALADIN models were evaluated against observed precipitation to provide a hydrological forecast. The AROME forecast performed better but still with a strong bias compared to observed precipitation. Further research is needed to link quantitative precipitation forecasts with hydrological models in this type of catchment.

Effects of hydrological variables on structuring morphological trait (cell size) of diatom community in a lowland river

Diatoms are widely used as bio-indicators in water quality monitoring and ecological status assessment. Compare to chemical variables, the effects of hydrological variables on diatom dynamics have been rarely reported. In this study, we aimed to examine dynamics of diatoms based on the classification of a morphological trait (cell size) and to investigate the relationship between the diatom community and selected chemical and hydrological variables. We collected 339 daily-mixed samples by using an auto-sampler, from which we recorded 113 diatom taxa from 45 genera. Analysis of morphological trait showed variations among cell size classes throughout the year with big cells in winter and spring while small cells dominated in summer and autumn. Redundancy analysis (RDA) and partial RDA indicated that chemical and hydrological variables could jointly explain 50.2% of the diatom community variation, in which chemical variables explain 36.5% and hydrological variables explain 12.9%. The most important chemical variables were Orthophosphate-phosphorus (PO4-P) and Silicon (Si2+) concentrations, while the most significant hydrological variables were antecedent precipitation index (API) and base flow. We conclude that besides chemical variables (nutrients and metal ions) hydrological variables showed also remarkable effects on the diatom community variation, especially during wet conditions. A key implication of our findings for freshwater management is that future bio-monitoring protocols should take hydrological variables into consideration.

Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model

AbstractWe develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root‐mean‐square errors = 0.13–0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai‐Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.