scholarly journals Incorporating Antecedent Soil Moisture into Streamflow Forecasting

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 50 ◽  
Author(s):  
Abdoul Oubeidillah ◽  
Glenn Tootle ◽  
Thomas Piechota
Keyword(s):  
Soil Moisture ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Antecedent Soil Moisture ◽  
Natural Resource Conservation ◽  
The North ◽  
Forecasting Methods ◽  
Natural Resource Conservation Service

This study incorporates antecedent (preceding) soil moisture into forecasting streamflow volumes within the North Platte River Basin, Colorado/Wyoming (USA). The incorporation of antecedent soil moisture accounts for infiltration and can improve streamflow predictions. Current Natural Resource Conservation Service (NRCS) forecasting methods are replicated, and a comparison is drawn between current NRCS forecasts and proposed forecasting methods using antecedent soil moisture. Current predictors used by the NRCS in regression-based streamflow forecasting include precipitation, streamflow persistence (previous season streamflow volume) and snow water equivalent (SWE) from SNOTEL (snow telemetry) sites. Proposed methods utilize antecedent soil moisture as a predictor variable in addition to the predictors noted above. A decision system was used to segregate data based on antecedent soil moisture conditions (e.g., dry, wet or normal). Principal Components Analysis and Stepwise Linear Regression were applied to generate streamflow forecasts, and numerous statistics were determined to measure forecast skill. The results show that when incorporating antecedent soil moisture, the “poor” forecasts (i.e., years in which the NRCS forecast differed greatly from the observed value) were improved, while the overall forecast skill remains unchanged. The research presented shows the need to increase the monitoring and collection of soil moisture data in mountainous western U.S. watersheds, as this parameter results in improved forecast skill.

scholarly journals Informing Hydrometric Network Design for Statistical Seasonal Streamflow Forecasts

2013 ◽  
Vol 14 (5) ◽  
pp. 1587-1604 ◽  
Author(s):  
Eric A. Rosenberg ◽  
Andrew W. Wood ◽  
Anne C. Steinemann
Keyword(s):  
Soil Moisture ◽  
Network Design ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Forecast Skill ◽  
Statistical Prediction ◽  
Moisture Sensor ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract A hydrometric network design approach is developed for enhancing statistical seasonal streamflow forecasts. The approach employs gridded, model-simulated water balance variables as predictors in equations generated via principal components regression in order to identify locations for additional observations that most improve forecast skill. The approach is applied toward the expansion of the Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network in 24 western U.S. basins using two forecasting scenarios: one that assumes the currently standard predictors of snow water equivalent and water year-to-date precipitation and one that considers soil moisture as an additional predictor variable. Resulting improvements are spatially and temporally analyzed, attributed to dominant predictor contributions, and evaluated in the context of operational NRCS forecasts, ensemble-based National Weather Service (NWS) forecasts, and historical as-issued NRCS/NWS coordinated forecasts. Findings indicate that, except for basins with sparse existing networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where SNOTEL stations are traditionally situated. The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.

Preliminary investigation of emerging suburban landsliding in Gisborne, New Zealand

2022 ◽  
pp. qjegh2021-087
Author(s):  
Matthew E. Cook ◽  
Martin S. Brook ◽  
Jon Tunnicliffe ◽  
Murry Cave ◽  
Noah P. Gulick
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
High Intensity ◽  
Preliminary Investigation ◽  
Rainfall Event ◽  
Slope Instability ◽  
Antecedent Soil Moisture ◽  
Field Surveys ◽  
Coastal City ◽  
The North

Recently uplifted, soft Pleistocene sediments in northern New Zealand are particularly vulnerable to landsliding because they are often underlain by less permeable, clay-rich Neogene mudstone/siltstone rocks. Typically, instability is rainfall-induced, often due to a high intensity rainfall event from extra-tropical cyclones, following wetter months when antecedent soil moisture has increased. Using remote sensing, field surveys and laboratory testing, we report on some emerging slope instability hazards in the eastern suburbs of the coastal city of Gisborne, on the North Island. Retrogressive failure of the main landslide (at Wallis Road) is ongoing and has already led to the abandonment of one home, while an adjacent landslide (at Titirangi Drive) appears to be in an incipient phase of failure. The Wallis Road landslide has been particularly active from mid-2017, with slumping of the headscarp area transitioning to a constrained mudflow downslope, which then descends a cliff before terminating on the beach. In contrast, the incipient Titirangi Drive landslide at present displays much more subtle effects of deformation. While activity at both landslides appears to be linked to rainfall-induced increases in soil moisture, this is due to the effects of prolonged periods of rainfall rather than the passage of high intensity cyclonic storms.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Quantitative assessment of surface runoff at arid region: a case study in the Middle of Nile Delta

2019 ◽  
Vol 43 (1) ◽  
Author(s):  
E. S. Mohamed ◽  
Abdel-Aziz Belal ◽  
Mohamed Abu-hashim
Keyword(s):  
Surface Runoff ◽  
Urban Areas ◽  
Nile Delta ◽  
Curve Number ◽  
Runoff Water ◽  
Natural Resource Conservation ◽  
The North ◽  
Natural Resource Conservation Service ◽  
Gis Tools ◽  
Geomorphic Units

Abstract Background Evaluation of surface runoff is an essential factor in the precision water and soil conservation management through their main extreme impacts on soil properties. The natural resource conservation service curve number model (NRCS-CN) model is used to estimate the magnitude of runoff. Collected topographic data is used to explain the effects of slope variation on water retention and surface runoff. Twenty-eight soil profiles are prepared in Nile delta, Egypt to cover different geomorphic units and hydrological soil groups in the study area. Results The results revealed that the highest value of surface runoff was distinguished close to the urban area and ranges between 40 and 50 mm. In urban areas, the surfaces are paved and there are no infiltration of water. Consequently, the runoff water directly flows to the storm channels. Runoff values ranging between 30 and 40 mm occurred at the north of the study area. The sloping surface and the nature of the clay soil contributed to generate more runoff than do lowland areas. Conclusion The study presented and tested the hydric runoff estimation based-model on the integrating of hydric balance parameters. The GIS tools analyze and compose these parameters to perform an indirect method for the quantity of water that results in direct surface runoff flow. This method helps to gain clear imaging of the surface runoff risks in the study area.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 10 (2) ◽  
pp. 1987-2013 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Lead Time ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Hydrologic Prediction

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic prediction skill at seasonal lead times (i.e. 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs – primarily the state of initial soil moisture and snow) and seasonal climate forecast skill (FS). In this study we quantify the contributions of IHCs and FS to seasonal hydrologic prediction skill globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the Variable Infiltration Capacity (VIC) macroscale hydrology model, one based on Ensemble Streamflow Prediction (ESP) and another based on Reverse-ESP (rESP), both for a 47 yr reforecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts obtained from each experiment with a control simulation forced with observed atmospheric forcings over the reforecast period and estimate the ratio of Root Mean Square Error (RMSE) of both experiments for each forecast initialization date and lead time. We find that in general, the contributions of IHCs are greater than the contribution of FS over the Northern (Southern) Hemisphere during the forecast period starting in October and January (April and July). Over snow dominated regions in the Northern Hemisphere the IHCs dominate the CR forecast skill for up to 6 months lead time during the forecast period starting in April. Based on our findings we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Estimation of surface runoff using NRCS curve number in some areas in northwest coast, Egypt

2020 ◽  
Vol 167 ◽  
pp. 02002
Author(s):  
E.S. Mohamed ◽  
M.A. Abdellatif ◽  
Sameh Kotb Abd-Elmabod ◽  
M.M.N. Khalil
Keyword(s):  
Surface Runoff ◽  
Water Retention ◽  
Agricultural Development ◽  
Curve Number ◽  
Temporal Variations ◽  
Northwest Coast ◽  
Natural Resource Conservation ◽  
The North ◽  
Natural Resource Conservation Service ◽  
Service Curve

The sustainable agricultural development in the northwest coast of Egypt suffers constantly from the effects of surface runoff. Moreover, there is an urgent need by decision makers to know the effects of runoff. So the aim of this work is to integrate remote sensing and field data and the natural resource conservation service curve number model (NRCS-CN).using geographic information systems (GIS) for spatial evaluation of surface runoff .CN approach to assessment the effect of patio-temporal variations of different soil types as well as potential climate change impact on surface runoff. DEM was used to describe the effects of slope variables on water retention and surface runoff volumes. In addition the results reflects that the magnitude of surface runoff is associated with CN values using NRCS-CN model . The average of water retention ranging between 2.5 to 3.9m the results illustrated that the highest value of runoff is distinguished around the urban area and its surrounding where it ranged between 138 - 199 mm. The results show an increase in the amount of surface runoff to 199 mm when rainfall increases 200 mm / year. The north of the area may be exposed to erosion hazards more than the south and a change in the soil quality may occur in addition to the environmental imbalance in the region.

scholarly journals RECONSTRUCTIONS OF HYDROLOGIC VARIABLES IN THE NORTH PLATTE RIVER BASIN

2020 ◽  
Vol 6 (2) ◽  
pp. 59-71
Author(s):  
Sally Rose Anderson ◽  
Amanda Bowen ◽  
Glenn Tootle ◽  
Abdoul Oubeidillah
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Platte River ◽  
The North ◽  
Hydrologic Variability ◽  
Snow Water ◽  
Moisture Sensitive ◽  
Platte River Basin

Reconstructions of hydrologic variables are commonly created using tree-ring chronologies (TRCs) to generate information about historic climate and potential future variability. This study used TRCs to reconstruct annual streamflow, April 1st Snow Water Equivalent (SWE), and soil moisture in the North Platte River Basin (NPRB). Stepwise linear regression was performed to determine which of the 55 moisture sensitive TRCs were the best predictors of hydrologic variation. The regressions explained 63% of the variability in streamflow, 55% of the variability in SWE, and 66% of the variability in soil moisture. This study then maximized the overlapping period of records which resulted in a decrease in the percent of variability explained but indicated that the regression models were stable for long reconstruction periods. This study successfully reconstructed all three hydrologic variables for NPRB to 1438 or earlier. Temporal wet and dry periods for streamflow and SWE were closely aligned while soil moisture did not follow similar temporal patterns. This was likely due to a natural “lag” between soil moisture and streamflow / SWE given soil moisture tends to retain antecedent signals. The availability of reconstructed hydrologic data in NPRB allows for a better understanding of the long-term hydrologic variability in the region.

scholarly journals Valuing Enhanced Hydrologic Data and Forecasting for Informing Hydropower Operations

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2260
Author(s):  
Han Guo ◽  
Martha Conklin ◽  
Tessa Maurer ◽  
Francesco Avanzi ◽  
Kevin Richards ◽  
...  
Keyword(s):  
Climate Change ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Sensor Data ◽  
Missed Opportunities ◽  
The North ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Spatially Distributed ◽  
Snow Water

Climate change is rapidly modifying historic river flows and snowpack conditions in the Sierra Nevada in California and other seasonally snow-covered mountains. Statistical forecasting methods based on regressing summer flow against spring snow water equivalent, precipitation, and antecedent runoff are thus becoming increasingly inadequate for water-resources decision making, which can lead to missed opportunities in maximizing beneficial uses, including the value of hydropower resources. An enhanced forecasting method using a process-based model and spatially distributed wireless sensor data offers more accurate runoff forecasts. In this paper, we assessed the forecasting accuracy of these two forecasting methods by applying them to two tributaries within the North Fork Feather River basin in California. The result shows the enhanced forecasting method having better accuracy than the statistical model. In addition, a hydropower simulation showed a considerable increase in energy value with the enhanced forecasting informing reservoir operations. The investment analysis on applying this method shows an average internal rate of return of 31% across all scenarios, making this forecasting method an attractive way to better inform water-related decisions for hydropower generation in the context of climate change.

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