Sensitivity based soil moisture assimilation for improved streamflow forecast using a novel Forward Sensitivity Method (FSM) approach

2022 ◽  
Author(s):  
R Visweshwaran ◽  
RAAJ Ramsankaran ◽  
T I Eldho ◽  
S Lakshmivarahan
Keyword(s):  
Soil Moisture ◽  
Streamflow Forecast ◽  
Sensitivity Method

scholarly journals Dynamic Data Assimilation for Improved Streamflow Forecast Using Sensitive Soil Moisture Observations

2022 ◽  
Author(s):  
R Visweshwaran ◽  
Raaj Ramsankaran ◽  
TI Eldho ◽  
S. Lakshmivarahan
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Dynamic Data ◽  
Streamflow Forecast

In-situ soil moisture data improve seasonal streamflow forecast accuracy in rainfall-dominated watersheds

2020 ◽  
Vol 590 ◽  
pp. 125404
Author(s):  
Briana M. Wyatt ◽  
Tyson E. Ochsner ◽  
Erik S. Krueger ◽  
Eric T. Jones
Keyword(s):  
Soil Moisture ◽  
Forecast Accuracy ◽  
Streamflow Forecast ◽  
Soil Moisture Data ◽  
Seasonal Streamflow

scholarly journals Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Randal D. Koster ◽  
Gregory K. Walker ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Forecast Skill ◽  
Surface Model ◽  
Streamflow Forecast ◽  
Continental Scale ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
Soil Moisture Initialization

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

scholarly journals Influence of bias correction of meteorological and streamflow forecast on hydrological prediction in India

2021 ◽  
Author(s):  
Amar Deep Tiwari ◽  
Parthasarathi Mukhopadhyay ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Lead Time ◽  
Root Zone ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Post Processing ◽  
Streamflow Prediction ◽  
Streamflow Forecast ◽  
Hydrological Prediction

AbstractThe efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecast and streamflow post-processing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root zone soil moisture in the Narmada basin (drainage area: 97,410 km2), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June-September) season for 2000-2018 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1-3 day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root mean squared error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1-3-day lead time. We used Empirical Quantile Mapping (EQM) to bias correct precipitation forecast. The bias correction of precipitation forecast resulted in significant improvement in the precipitation forecast skill. Runoff and root zone soil moisture forecast was also significantly improved due to bias correction of precipitation forecast where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecast did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecast performs better than the streamflow forecast simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecast and post-processing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40% to 5%).

Predictability of Seasonal Streamflow and Soil Moisture in National Water Model and a Humid Alabama–Coosa–Tallapoosa River Basin

2020 ◽  
Vol 21 (7) ◽  
pp. 1447-1467 ◽  
Author(s):  
Yanan Duan ◽  
Sanjiv Kumar
Keyword(s):  
Soil Moisture ◽  
Water Model ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Streamflow Forecast ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
Seasonal Streamflow ◽  
Soil Moisture Variability ◽  
National Water

AbstractThis study investigates the potential predictability of streamflow and soil moisture in the Alabama–Coosa–Tallapoosa (ACT) river basin in the southeastern United States. The study employs the state-of-the-art National Water Model (NWM) and compares the effects of initial soil moisture condition with those of seasonal climate anomalies on streamflow and soil moisture forecast skills. We have designed and implemented seasonal streamflow forecast ensemble experiments following the methodology suggested by Dirmeyer et al. The study also compares the soil moisture variability in the NWM with in situ measurements and remote sensing data from the Soil Moisture Active and Passive (SMAP) satellite. The NWM skillfully simulates the observed streamflow in the ACT basin. The soil moisture variability is 46% smaller in the NWM compared with the SMAP data, mainly due to a weaker amplitude of the seasonal cycle. This study finds that initial soil moisture condition is a major source of predictability for the seasonal streamflow forecast. The contribution of the initial soil moisture condition is comparable or even higher than that of seasonal climate anomaly effects in dry seasons. In the boreal summer season, the initial soil moisture condition contributes to 65% and 48% improvements in the seasonal streamflow and soil moisture forecast skills, respectively. This study attributes a greater improvement in the streamflow forecast skill to the lag effects between the soil moisture and streamflow anomalies. The results of this study can inform the development and improvement of the operational streamflow forecasting system.

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

SOIL MOISTURE TRANSECT DATA (FIFE)

1994 ◽  
Author(s):  
E. T. ENGMAN ◽  
J. R. WANG
Keyword(s):  
Soil Moisture

SOIL MOISTURE RELEASE DATA (FIFE)

1994 ◽  
Author(s):  
T. DEMETRIADES-SHAH ◽  
E. T. KANEMASU
Keyword(s):  
Soil Moisture ◽  
Release Data
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