The efficacy of seasonal terrestrial water storage forecasts for predicting vegetation activity over Africa

2021 ◽  
Author(s):  
Benjamin I Cook ◽  
Kimberly Slinski ◽  
Christa Peters-Lidard ◽  
Amy McNally ◽  
Kristi Arsenault ◽  
...  
Keyword(s):  
Regression Model ◽  
Southern Africa ◽  
Water Storage ◽  
Dry Season ◽  
Forecast Skill ◽  
Terrestrial Water Storage ◽  
Wet Season ◽  
Area Index ◽  
Vegetation Activity ◽  
Terrestrial Water

AbstractTerrestrial water storage (TWS) provides important information on terrestrial hydroclimate and may have value for seasonal forecasting because of its strong persistence. We use the NASA Hydrological Forecast and Analysis System (NHyFAS) to investigate TWS forecast skill over Africa and assess its value for predicting vegetation activity from satellite estimates of leaf area index (LAI). Forecast skill is high over East and Southern Africa, extending up to 3–6 months in some cases, with more modest skill over West Africa. Highest skill generally occurs during the dry season or beginning of the wet season when TWS anomalies from the previous wet season are most likely to carry forward in time. In East Africa, this occurs prior to and during the transition into the spring “Long Rains” from January–March, while in Southern Africa this period of highest skill starts at the beginning of the dry season in April and extends through to the start of the wet season in October. TWS is highly and positively correlated with LAI, and a logistic regression model shows high cross-validation skill in predicting above or below normal LAI using TWS. Combining the LAI regression model with the NHyFAS forecasts, 1-month lead LAI predictions have high accuracy over East and Southern Africa, with reduced but significant skill at 3-month leads over smaller sub-regions. This highlights the potential value of TWS as an additional source of information for seasonal forecasts over Africa, with direct applications to some of the most vulnerable agricultural regions on the continent.

scholarly journals Estimation of hydrological drought recovery based on GRACE water storage deficit

2019 ◽  
Author(s):  
Alka Singh ◽  
John T. Reager ◽  
Ali Behrangi
Keyword(s):  
Recovery Period ◽  
Water Storage ◽  
Storage Conditions ◽  
Precipitation Anomaly ◽  
Precipitation Forecast ◽  
Climate Extreme ◽  
Terrestrial Water Storage ◽  
Wet Season ◽  
Drought Recovery ◽  
Terrestrial Water

Abstract. Drought is a natural climate extreme phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data with historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill. Three different precipitation scenarios are simulated by using a standard deviation in climatology. Precipitation scenarios are convolved with precipitation deficit estimates to calculate best-estimate of a drought recovery period. The results show that in the regions of strong seasonal amplitude (like monsoon belt) drought continues even with the above-normal precipitation until its wet season. Historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated 80% similar recovery period as observed by the GRACE.

The Impact of Noah-MP Physical Parameterizations on Modeling Water Availability during Droughts in the Texas-Gulf Region

2021 ◽  
Author(s):  
Wen-Ying Wu ◽  
Zong-Liang Yang ◽  
Michael Barlage
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Availability ◽  
Water Storage ◽  
Rooting Depth ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Area Index ◽  
Terrestrial Water ◽  
The Impact

AbstractTexas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multi-parameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

scholarly journals Hydrological Control of Vegetation Greenness Dynamics in Africa: A Multivariate Analysis Using Satellite Observed Soil Moisture, Terrestrial Water Storage and Precipitation

Land ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 15 ◽  
Author(s):  
Sabastine Ugbaje ◽  
Thomas Bishop
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Water Storage ◽  
Important Variable ◽  
Delayed Response ◽  
Terrestrial Water Storage ◽  
Vegetation Greenness ◽  
Vegetation Activity ◽  
Terrestrial Water ◽  
Hydrological Variables

Vegetation activity in many parts of Africa is constrained by dynamics in the hydrologic cycle. Using satellite products, the relative importance of soil moisture, rainfall, and terrestrial water storage (TWS) on vegetation greenness seasonality and anomaly over Africa were assessed for the period between 2003 and 2015. The possible delayed response of vegetation to water availability was considered by including 0–6 and 12 months of the hydrological variables lagged in time prior to the vegetation greenness observations. Except in the drylands, the relationship between vegetation greenness seasonality and the hydrological measures was generally strong across Africa. Contrarily, anomalies in vegetation greenness were generally less coupled to anomalies in water availability, except in some parts of eastern and southern Africa where a moderate relationship was evident. Soil moisture was the most important variable driving vegetation greenness in more than 50% of the areas studied, followed by rainfall when seasonality was considered, and by TWS when the monthly anomalies were used. Soil moisture and TWS were generally concurrent or lagged vegetation by 1 month, whereas precipitation lagged vegetation by 1–2 months. Overall, the results underscore the pre-eminence of soil moisture as an indicator of vegetation greenness among satellite measured hydrological variables.

scholarly journals Multi-decadal hydrologic change and variability in the Amazon River basin: understanding terrestrial water storage variations and drought characteristics

2019 ◽  
Vol 23 (7) ◽  
pp. 2841-2862 ◽  
Author(s):  
Suyog Chaudhari ◽  
Yadu Pokhrel ◽  
Emilio Moran ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Water Deficit ◽  
River Basin ◽  
Water Storage ◽  
Dry Season ◽  
Amazon River ◽  
Terrestrial Water Storage ◽  
Amazon River Basin ◽  
Lulc Change ◽  
Continental Scale ◽  
Terrestrial Water

Abstract. We investigate the interannual and interdecadal hydrological changes in the Amazon River basin and its sub-basins during the 1980–2015 period using GRACE satellite data and a physically based, 2 km grid continental-scale hydrological model (LEAF-Hydro-Flood) that includes a prognostic groundwater scheme and accounts for the effects of land use–land cover (LULC) change. The analyses focus on the dominant mechanisms that modulate terrestrial water storage (TWS) variations and droughts. We find that (1) the model simulates the basin-averaged TWS variations remarkably well; however, disagreements are observed in spatial patterns of temporal trends, especially for the post-2008 period. (2) The 2010s is the driest period since 1980, characterized by a major shift in the decadal mean compared to the 2000s caused by increased drought frequency. (3) Long-term trends in TWS suggest that the Amazon overall is getting wetter (1.13 mm yr−1), but its southern and southeastern sub-basins are undergoing significant negative TWS changes, caused primarily by intensified LULC changes. (4) Increasing divergence between dry-season total water deficit and TWS release suggests a strengthening dry season, especially in the southern and southeastern sub-basins. (5) The sub-surface storage regulates the propagation of meteorological droughts into hydrological droughts by strongly modulating TWS release with respect to its storage preceding the drought condition. Our simulations provide crucial insight into the importance of sub-surface storage in alleviating surface water deficit across Amazon and open pathways for improving prediction and mitigation of extreme droughts under changing climate and increasing hydrologic alterations due to human activities (e.g., LULC change).

scholarly journals Multi-decadal Hydrologic Change and Variability in the Amazon River Basin: Understanding Terrestrial Water Storage Variations and Drought Characteristics

2019 ◽  
Author(s):  
Suyog Chaudhari ◽  
Yadu Pokhrel ◽  
Emilio Moran ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Water Deficit ◽  
River Basin ◽  
Water Storage ◽  
Dry Season ◽  
Amazon River ◽  
Terrestrial Water Storage ◽  
Amazon River Basin ◽  
Continental Scale ◽  
South Eastern ◽  
Terrestrial Water

Abstract. We investigate the interannual and interdecadal hydrological changes in the Amazon river basin and its sub-basins during 1980–2015 period, using GRACE satellite data and a physically-based, 2-km grid continental scale hydrological model (Leaf-Hydro-Flood) that incorporates a prognostic groundwater scheme and the effects of land use land cover change (LULC). The analyses focus on the dominant mechanisms that modulate terrestrial water storage (TWS) variations and droughts. Our results indicate that (1) the model simulates the basin-averaged TWS variations remarkably well, however, disagreements are observed in spatial patterns of temporal trends for post-2008 period, (2) the 2010s is the driest period since 1980, characterized by a major shift in decadal mean compared to 2000s due to the increased frequency of droughts, (3) long-term trends in TWS suggests that the Amazon as a whole is getting wetter (1.13 mm/y), but its southern and south-eastern sub-basins are facing significant negative TWS trends, caused primarily by intensified LULC changes, (4) increasing divergence between dry season total water deficit (TWD) and TWS release (TWS-R) suggest a strengthening dry season, especially in the southern and south-eastern sub-basins, and (5) the sub-surface storage regulates the propagation of meteorological droughts into hydrological droughts by strongly modulating TWS release with respect to its storage preceding the drought condition. Our simulations provide crucial insight on the importance of sub-surface storage in alleviating surface water deficit across Amazon and open pathways for improving prediction and mitigation of extreme droughts under changing climate and increasing hydrologic alterations due to human activities (e.g., LULC change).

Evaluating terrestrial water storage variations from regionally constrained GRACE mascon data and hydrological models over Southern Africa – preliminary results

2010 ◽  
Vol 31 (14) ◽  
pp. 3899-3912 ◽  
Author(s):  
Pernille E. Krogh ◽  
Ole B. Andersen ◽  
Claire I. B. Michailovsky ◽  
Peter Bauer-Gottwein ◽  
David D. Rowlands ◽  
...  
Keyword(s):  
Southern Africa ◽  
Water Storage ◽  
Hydrological Models ◽  
Terrestrial Water Storage ◽  
Preliminary Results ◽  
Terrestrial Water

Benchmark, predictability, and forecast skill of terrestrial water storage based on CMIP6 decadal hindcasts and land surface ensemble simulations

2020 ◽  
Author(s):  
Enda Zhu ◽  
Xing Yuan
Keyword(s):  
Initial Conditions ◽  
Water Storage ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Climate Forecast ◽  
Terrestrial Water Storage ◽  
Ensemble Simulations ◽  
Climate Forecasts ◽  
Terrestrial Water ◽  
Decadal Climate

<p><span>Terrestrial water storage (TWS), including surface water storage, soil water storage, and groundwater storage, is critical for the global hydrological cycle and freshwater resources. A reliable decadal prediction of TWS can provide valuable information for sustainable managements of water resources and infrastructures in the face of climate change. Generally, the hydrological predictability mainly comes from two sources, i.e., initial conditions and boundary conditions. To date, the dependence of TWS forecast skill on the accuracy of initial hydrological conditions and decadal climate forecasts is not clear, and the benchmark skill remains unknown. In this work, we use decadal climate hindcasts from CMIP and perform hydrological ensemble simulations to estimate a baseline decadal forecast skill containing the two predictability sources information for TWS over global major river basins with an elasticity framework that considers varying skill of initial conditions and climate forecasts. With the incorporation of decadal climate forecast, our benchmark skill for TWS incorporated is significantly higher than initial conditions-based forecast skill over 25% and 31% basins for the leads of 1–4 and 3–6 years, especially over mid- and high-latitudes. Although the decadal precipitation forecast skill based on individual model is limited, the ensemble forecasts from multiple climate models are better than individuals. In addition, the standardized precipitation index (SPI) predictability and forecast skill from the latest CMIP6 decadal hindcast data are being investigated. Preliminary results suggest that predictability and forecast skill of SPI are positively correlated in general, and the predictability is higher than forecast skill, indicating the room for improving hydro-climate forecast. Our findings provide a new benchmark for verifying the success of decadal TWS forecasts and imply the possibility of improving decadal hydrological forecasts by using dynamical climate prediction information which still has room for improvement.</span></p>

scholarly journals A time series assessment of terrestrial water storage and its relationship with hydro-meteorological factors in Gilgit-Baltistan region using GRACE observation and GLDAS-Noah model

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Dostdar Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Najam Ul Hassan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Snow Water Equivalent ◽  
Gravity Data ◽  
Water Storage ◽  
Indus Basin ◽  
Flood Hazards ◽  
Terrestrial Water Storage ◽  
Satellite Gravity ◽  
Terrestrial Water ◽  
Gilgit Baltistan

AbstractMountains regions like Gilgit-Baltistan (GB) province of Pakistan are solely dependent on seasonal snow and glacier melt. In Indus basin which forms in GB, there is a need to manage water in a sustainable way for the livelihood and economic activities of the downstream population. It is important to monitor water resources that include glaciers, snow-covered area, lakes, etc., besides traditional hydrological (point-based measurements by using the gauging station) and remote sensing-based studies (traditional satellite-based observations provide terrestrial water storage (TWS) change within few centimeters from the earth’s surface); the TWS anomalies (TWSA) for the GB region are not investigated. In this study, the TWSA in GB region is considered for the period of 13 years (from January 2003 to December 2016). Gravity Recovery and Climate Experiment (GRACE) level 2 monthly data from three processing centers, namely Centre for Space Research (CSR), German Research Center for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL), System Global Land Data Assimilation System (GLDAS)-driven Noah model, and in situ precipitation data from weather stations, were used for the study investigation. GRACE can help to forecast the possible trends of increasing or decreasing TWS with high accuracy as compared to the past studies, which do not use satellite gravity data. Our results indicate that TWS shows a decreasing trend estimated by GRACE (CSR, GFZ, and JPL) and GLDAS-Noah model, but the trend is not significant statistically. The annual amplitude of GLDAS-Noah is greater than GRACE signal. Mean monthly analysis of TWSA indicates that TWS reaches its maximum in April, while it reaches its minimum in October. Furthermore, Spearman’s rank correlation is determined between GRACE estimated TWS with precipitation, soil moisture (SM) and snow water equivalent (SWE). We also assess the factors, SM and SWE which are the most efficient parameters producing GRACE TWS signal in the study area. In future, our results with the support of more in situ data can be helpful for conservation of natural resources and to manage flood hazards, droughts, and water distribution for the mountain regions.

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