Accumulated basin storage as a factor in the correlation structure of annual peak flows on the Red River

1986 ◽  
Vol 13 (3) ◽  
pp. 365-374 ◽  
Author(s):  
C. Booy ◽  
L. M. Lye

The well-established occurrence of exceptionally high floods on the Red River prior to the record of annual peak flows at Winnipeg is an important factor in the flood risk assessment for that city and for the entire Red River valley. But the weight given to this occurrence is quite dependent on the autocorrelation structure assumed for the spring peak time series. It is therefore important to decide whether the clustering of high peak flows, which can be observed in the record, is a mere chance phenomenon or indeed a characteristic of the runoff process. In earlier studies this clustering was found to be significant in a statistical sense. The present study aims at finding a physical explanation for this particular type of correlation structure. It presents the accumulated basin storage (ABS) as a physically based parameter that measures average soil moisture conditions in the drainage basin. The reconstructed record of ABS values just prior to the spring runoff shows a very high first-order autocorrelation coefficient. Relatively wet and relatively dry soil conditions therefore tend to persist over long periods. Since the magnitude of the spring peak is significantly affected by soil moisture conditions prior to snowmelt, the structure of the annual ABS time series can be expected to be reflected in the peak flow time series. This was found to be the case. The study thus supports earlier conclusions based on statistical evidence that the conventional assumption of serially independent spring peak floods seriously underestimates the flood risk for the City of Winnipeg and the Red River valley. Key words: accumulated basin storage, Red River floods, simulation, time series, clustering, streamflow persistence, serial correlation, flood risk.

1985 ◽  
Vol 12 (1) ◽  
pp. 150-165 ◽  
Author(s):  
C. Booy ◽  
D. R. Morgan

The nearly 100 year record of spring flood peaks on the Red River at Winnipeg, Manitoba, shows a clustering of high annual peak flows that is possibly, but not likely, due to chance. A similar degree of clustering has been observed in other long-term geophysical records. It can be measured by means of the Hurst statistic. Clustering increases the uncertainty in the parameters of the probability distribution of peak flows estimated from the record. As such it profoundly affects the weight that must be given to the unusually high historical floods that preceded the period of record, in particular the 1826 and the 1852 floods. Incorporating this historical information in the probability analysis requires a time series model that tends to produce the appropriate degree of clustering. A fractional noise model was adopted for this purpose. Bayes' theorem was then used to update the distribution parameters, obtained from the record, with the additional information about the historical floods. The result shows the flood risk to the City of Winnipeg and the Red River Valley to be substantially higher than was estimated by conventional methods that assume serial independence of the peak flows. Key words: Red River floods, flood risk, historical floods, Hurst phenomenon, fractional noise, Bayesian probability distribution, Bayesian updating, time series.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Navid Ghajarnia ◽  
Zahra Kalantari ◽  
René Orth ◽  
Georgia Destouni

AbstractSoil moisture is an important variable for land-climate and hydrological interactions. To investigate emergent large-scale, long-term interactions between soil moisture and other key hydro-climatic variables (precipitation, actual evapotranspiration, runoff, temperature), we analyze monthly values and anomalies of these variables in 1378 hydrological catchments across Europe over the period 1980–2010. The study distinguishes results for the main European climate regions, and tests how sensitive or robust they are to the use of three alternative observational and re-analysis datasets. Robustly across the European climates and datasets, monthly soil moisture anomalies correlate well with runoff anomalies, and extreme soil moisture and runoff values also largely co-occur. For precipitation, evapotranspiration, and temperature, anomaly correlation and extreme value co-occurrence with soil moisture are overall lower than for runoff. The runoff results indicate a possible new approach to assessing variability and change of large-scale soil moisture conditions by use of long-term time series of monitored catchment-integrating stream discharges.


2020 ◽  
Vol 12 (17) ◽  
pp. 2733 ◽  
Author(s):  
Jashvina Devadoss ◽  
Nicola Falco ◽  
Baptiste Dafflon ◽  
Yuxin Wu ◽  
Maya Franklin ◽  
...  

In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.


2005 ◽  
Vol 9 (4) ◽  
pp. 431-448 ◽  
Author(s):  
H. Hlavcova ◽  
S. Kohnova ◽  
R. Kubes ◽  
J. Szolgay ◽  
M. Zvolensky

Abstract. Since medium and long-term precipitation forecasts are still not reliable enough, rough estimates of the degree of the extremity of forthcoming flood events that might occur in the course of dangerous meteorological situations approaching a basin could be useful to decision-makers as additional information for flood warnings. One approach to answering such a problem is to use real-time data on the soil moisture conditions in a catchment in conjunction with estimates of the extremity of the future rainfall and experience with the basin's behaviour during historical floods. A scenario-based method is proposed for such a future flood risk estimation, based on an a priori evaluation of the extremity of hypothetical floods generated by combinations of synthetic extreme precipitation and previously observed antecedent pre-flood basin saturations. The Hron river basin, located in central Slovakia, was chosen as the pilot basin in the case study. A time series of the basin's average daily precipitation was derived using spatial interpolation techniques. A lumped HBV-type daily conceptual rainfall-runoff model was adopted for modelling runoff. Analysis of the relationship of the modelled historical pre-flood soil moisture and flood causing-precipitation revealed the independence of both quantities for rainfall durations lasting 1 to 5 days. The basin's average annual maximum 1 to 5 day precipitation depths were analysed statistically and synthetic extreme precipitation scenarios associated with rainfall depths with return periods of 5, 20, 50 and 100 years, durations of 1 to 5 days and temporal distribution of extreme rainfall observed in the past were set up for runoff simulation. Using event-based flood simulations, synthetic flood waves were generated for random combinations of the rainfall scenarios and historical pre-flood soil moisture conditions. The effect of any antecedent basin saturation on the extremity of floods was quantified empirically and critical values of the basin saturation leading to floods with a higher return period than the return period of precipitation were identified. A method for implementing such critical values into flood risk warnings in a hydrological forecasting and warning system in the basin was suggested.


2020 ◽  
Author(s):  
Lakshmi Girija ◽  
Sudheer Kulamullaparambathu

<p>Extensive research is being carried out in developing new calibration procedures for improving the efficacy of hydrologic models. Considering the simulation period into separate wet and dry periods, and performing discrete calibration on each of them has resulted in improvement in model performance, especially during dry periods. In this procedure, it is envisaged that by splitting the time period into wet and dry, the temporal variability of soil moisture, which play a major role in maintaining the water balance of the catchment, is accounted. The discretely calibrated data is then recombined to form the entire time series. However, while recombining the discretely calibrated time periods, the physics of the hydrological processes, at the time of transition from one period to the other, may show abrupt variations. In addition, the short spells of wetness and dryness within this partitioned period, which influences the soil saturation, may not get effectively simulated. This study proposes division of simulation period into wet and dry spells considering the state of saturation of the watershed. This is achieved by clustering the time series of the data using the antecedent precipitation and the soil moisture conditions. A supervised Gustafson-Kessel clustering technique is employed for the same. Subsequently, a relationship between the precipitation, the daily change in soil moisture and a selected model parameter is established for all the cluster transitions and incorporated into the model structure. The proposed methodology is tested using a grid based model with six parameters, on Riesel watershed, Texas, USA. The results indicate that clusters formed are unique, with no fixed duration and no repetitive patterns across the entire simulation period. For preliminary analysis, only one parameter is dynamically varied depending on the incoming rainfall. The performance of the refined model (NSE = 0.85) over the conventional static parameter model (NSE = 0.83), though not significant, indicate that better process representation can aid in improving model simulations. It is noted that this method eliminates the abrupt variation of soil moisture across the wet and dry periods, as the simulation is continuous.</p>


2021 ◽  
pp. 1-8

Summary. Following the commissioning of the Gabčíkovo (Bős) hydroelectric power plant in 1992, a monitoring program was launched to assess the agricultural and forestry consequences of the diversion of the Danube into a newly built derivation channel in the Žitný ostrov (Csallóköz) and Szigetköz areas. Prior to the Danube diversion, groundwater played a significant role in the water supply of plants, therefore it is of primary importance to monitor the changes in groundwater levels and soil moisture. Correlation between the groundwater depth and soil moisture time series taken at four measurement points of Szigetköz (T-03, T-04, T-09, T-16) between 1995 and 2012 was analysed. Average and extreme water levels (quartiles 1 and 4) were examined for the 18-year time series, in which 2nd and 3rd quartiles of the groundwater levels were treated together as characteristic water level. It was found that groundwater significantly correlated with soil moisture storage below the rooting zone of field crops. Összefoglalás. A Gabčíkovo (Bős) vízerőművet 1992-ben helyezték üzembe. A dunacsúnyi duzzasztó vize a bősi erőművön átfolyva a Szlovákiában épített vízlevezető csatornából 40 km után tért vissza a korábbi Duna főmederbe. A régi Duna főmederbe emiatt az elterelt szakaszon a korábbi vízmennyiség ötöde került. Minthogy mind a szlovákiai, mind a magyarországi mezőgazdasági és erdőterületek vízellátásában a talajvíz és a dunai árhullámok jelentős szerepet játszottak, 1995-től a Duna-elterelés hatásának felmérésére talajvízszint és talajnedvesség monitoring program indult a Csallóközben és a Szigetközben. A szlovák megfigyelések publikált anyagainak megállapításait és a Szigetközből két szántóföld, egy kaszálórét és egy nyárfaültetvény 1995 és 2012 közötti mérési adatait dolgoztuk fel. A talajvízmélység és a 10 cm-es talajrétegek mért térfogatszázalékos (v.%) nedvességtartalmából számított talajvízkészletek közötti korrelációt számítottuk. A 18 éves idősoron külön vizsgáltuk a jellemző, illetve a szélsőséges vízszintek (1. és 4. kvartilisek) hatását. A jellemző vízszintek hatásának vizsgálatához a talajvízszint értékek 2. és 3. kvartilisét egyben kezeltük. Megállapítottuk, hogy szignifikáns, ill. közel szignifikáns összefüggés csupán az átlagosnál a talajfelszínhez közelebbi (Q1) talajvízmélység esetén volt kimutatható mind a mély (T-03), mind a sekély talajrétegű (T-09) szántóföld 210–300 cm-es, illetve 120–140 cm-es talajszintjében. Vagyis a szántóföldi kultúrák számára az átlagos talajvízmélység nem jelentett vízpótlást. A régi Duna főmederhez közeli kaszálóréten (T-04) a talajvízmélység helyett a dunaremetei medervízszint adatok és a talajnedvességkészlet között még a 140 cm-es mélységben található kavicsos alapkőzet fölötti 20 cm-es talajrétegben sem volt jelentős kapcsolat. A mély talajrétegű (300 cm) erdészeti mérőhely (T-16) talajvízmélység és talajnedvességkészlet korrelációja csupán a 210–300 cm-es talajréteg esetében volt közel szignifikáns. A nyárültetvények fejlődéséhez szükséges éves 700–900 mm vízigény biztosítására emiatt a régi Duna főmederbe engedett többletvízre lenne szükség. A szántóföldi kultúrák terméshozama is elsősorban az adott év csapadékmennyisége és eloszlása szerint alakul. Amennyiben az időjárási feltételek kedvezőtlenek, megoldásként öntözni szükséges. Beszámoltunk továbbá arról, hogy két éve négy mérőhely üzemel, ami a naponta óránként mért 6 órás átlag talajnedvesség-adatokat gyűjti. A folyamatos talajnedvesség-adatgyűjtés célja az időjárás, a növényi vízfelhasználás és a talajvízből történő nedvesítés nyomon követése és a talajvízforgalom-modell leírásának a kontrollja. A közeljövő feladata az évente 12-14 alkalommal az ezeken a mérőhelyeken is gyűjtött kapacitívszondás és a folyamatos nedvességmérési eredmények megfeleltetése, minthogy a bemutatott közel azonos példa mellett több helyen és mélységben időben párhuzamos módon változik ugyan a kétféle érték, azonban akár több, mint 5 v.% különbséggel.


Soil Systems ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Umesh Acharya ◽  
Aaron L. M. Daigh ◽  
Peter G. Oduor

Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.


2021 ◽  
Author(s):  
Keshav Parameshwaran Shankara Mahadevan ◽  
Hartmut Holländer ◽  
Paul Bullock ◽  
Steven Frey ◽  
Timi Ojo

&lt;p&gt;Soil moisture is highly variable in space and time. Climate change is expected to increase the variation in precipitation that may cause more frequent extremes in soil moisture. This will have major impacts on agriculture and infrastructure. Hence, forecasting can help mitigate the impacts of soil moisture extremes by providing warning about upcoming extreme events. Accurate soil moisture forecasting will provide policymakers, farmers and other stakeholders more reliable information on crop yield potential and flood risk to improve decision making. &amp;#160;Real-time soil moisture monitoring and forecasting can be accomplished by utilizing a numerical modelling approach that consolidates various sources of weather and hydrological data to simulate soil moisture levels. Soil water movement is difficult to describe numerically for fine-textured soils. Additionally, soil water behaviour during freeze/thaw events are generally weakly described by numerical tools. This study addresses both problems and evaluates how soil moisture can be forecasted under the hydrologically challenging conditions of the Red River Valley using the Brunkild catchment within the Red River basin.&amp;#160; The Brunkild catchment represents a highly variable landscape cross-section that includes heavy clay soils of the Red River Valley through to the coarse-textured soils of the adjacent escarpment. Soil moisture levels were continuously monitored from June &amp;#8211; August 2020 using Sentek sensors which were installed at depths of 10 to 90 cm with 10 cm spacing, and with POGO sensors that were used to manually measure surface soil moisture levels at monthly intervals from June to August 2020. Climate variables were obtained from the RISMA (Real-time In-situ Soil Monitoring for Agriculture) stations present inside the catchment.&amp;#160; In addition to soil moisture data, surface water flow and groundwater data will also be used to aid with calibration and validation of a fully-integrated HydroGeoSphere (HGS) surface water &amp;#8211; groundwater model of the catchment. Preliminary results using MERRA 2 data as climate forcing showed a strong fit for all observations in sandy soils and a good fit for all observation in clay. The next simulations will use the observed weather data. The model will be recalibrated and then being used to forecast soil moisture in the Brunkild catchment for the coming 14 days for the 2021 growing season.&lt;/p&gt;


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